DEV Community: Spheron

Spheron's Matchmaking Mechanisms: Connecting GPU Users and Providers

SpheronStaff — Wed, 29 May 2024 13:36:55 +0000

In today's rapidly evolving digital landscape, the demand for GPU resources is skyrocketing – particularly given the ongoing surge in AI and machine learning applications. Traditional centralized GPU markets are struggling to keep pace with this demand, leading to increased costs and limited access.

To address these growing resource needs, Spheron has created a groundbreaking global compute network that ensures the efficient, cost-effective, and equitable distribution of GPU resources. Now, anyone can earn passive returns by lending their excess GPU power to Spheron Network – and become a vital part of the decentralized AI revolution!

Spheron’s decentralized market connects you to a worldwide user base that's ready to utilize providers’ excess compute power, no matter where they are. Let’s break down how it works.

Spheron’s Decentralized Compute Network

At the heart of Spheron's protocol lies the Decentralized Compute Network (DCN), a distributed framework where independent providers supply GPU and compute resources. This network ensures resilience, scalability, and accessibility, catering to the diverse needs of AI and ML projects. Central to the DCN is the Matchmaking Engine, which is designed to efficiently connect GPU users with providers.

Matchmaking Engine: Enabling Efficient, Automated Resource Allocation

Spheron's Matchmaking Engine is tasked with the essential role of orchestrating the dynamic allocation of GPU resources between deployment requests and provider nodes. This mechanism leverages the Actively Validated Services (AVS) framework from EigenLayer, which incorporates a sophisticated consensus algorithm to match deployment requests with the most suitable providers.

How it Works:

Provider Registration: Providers begin by registering their compute specs, region, tier, and other relevant details on the Provider Registry.

Provider Lookup and Bidding: When a user initiates a deployment request, the matchmaking engine broadcasts a PROVIDER_LOOKUP event. Providers that meet the deployment criteria submit their bids, detailing their capacity and pricing.

Consensus-Based Matching: The matchmaking algorithm evaluates these bids based on predefined parameters, such as geographic proximity, price, uptime, and reputation. Once a quorum is reached, the optimal provider is selected, and the matched order is updated in the Task Manager contract.

Lease Creation and Deployment: Following the provider selection, the Deployment Order contract establishes a lease with the provider, initiating the deployment process. The provider then configures and activates the server, with all relevant deployment details made available to the user.

Payment and Settlement: Once a user chooses a provider to deploy their workloads, all pertinent details are shared on-chain, marking the beginning of payment settlement based on the pricing set by the provider.

In addition to the above steps, Provider Nodes are also responsible for maintaining their node activity and chain state synchronization. Specific responsibilities include:

Deployment Management: Provider Nodes are responsible for the continuous management of deployments, including the initiation (CREATE_LEASE), update (UPDATE_LEASE), and termination (CLOSE_LEASE) of services. Each action is meticulously recorded on the blockchain, ensuring transparency and traceability.

Synchronization and Reliability: To maintain operational alignment with the network, Provider Nodes run RPC / Sequencer nodes to synchronize with the latest chain state. This synchronization is critical for participating in the bidding process and for ensuring the timely and successful execution of deployments.

Together, the above steps ensure the continual exchange of available GPU resources with end users within a seamless, low-cost environment.

Spheron’s Matchmaking Engine Benefits All Ecosystem Participants

Spheron's Matchmaking Engine provides multiple benefits to GPU providers and end users alike by offering a more transparent, cost-effective, and efficient alternative to traditional service providers like AWS or Google Cloud.

Key benefits include:

Welcome to the Future of Decentralized Compute

Spheron’s Matchmaking Engine allows anyone to play a vital role in the decentralized AI revolution, benefiting providers with new revenue streams and users with on-demand, customizable options. This approach current market constraints while catalyzing innovation and development in AI and machine learning – at a lower cost and higher performance levels than existing solutions.

Whether you want to access unlimited GPU power instantly and affordably or get paid to lend out your extra computing power, Spheron invites you to participate in a cutting-edge global compute network that is dynamic, scalable, and ready for tomorrow.

today and unlock your full earning potential!

How Spheron Empowers GPU Providers

SpheronStaff — Mon, 13 May 2024 15:50:25 +0000

While it's often stated that the demand for GPU power exceeds supply, this narrative overlooks the vast reservoir of underutilized GPUs outside centralized computing services. Many GPUs sit dormant, not because they aren't needed, but because they aren't accessible through traditional channels. The key to fueling the future of compute-intensive applications like AI lies in unlocking this immense computational potential precisely what Spheron lets you do.

Whether you are an individual GPU owner with a single high-performance card or run a data center with extensive resources, Spherons innovative platform presents a lucrative opportunity to monetize your hardware.

What is Spheron?

Spheron's decentralized architecture is built to maximize the utilization of your GPU resources. By joining the Spheron network, your equipment becomes part of a global compute framework, facilitating access to a wide market. This system is underpinned by Spherons Decentralized Compute Network (DCN), which ensures that all resource allocations are efficient and secure, optimizing your GPU's workload without compromising lifespan.

Key benefits include:

Passive Returns : Earn by renting out excess GPU resources, thereby extending the economic life of your computational resources.
Global Reach : Access a worldwide market of users, expanding your potential user base without the need for manual outreach or deployment.
Flexibility: Ability to adjust offerings in real-time based on demand and pricing.
Transparent and Fair Marketplace : Spherons blockchain-based architecture ensures transparency in transactions, pricing, and resource allocation, fostering a fair and open marketplace for compute resources.
Enhanced Security : Spheron takes security to the next level by utilizing Actively Validated Services (AVS), enhancing security, and restricting unauthorized access to private information from both the host and user sides.
Participation Rewards : Earn additional income based on a wide range of performance-based milestones, including consistent uptime and quality service.

In short, Spheron's decentralized platform offers a multitude of benefits tailored to GPU providers of all scales. By integrating your resources with Spheron, you tap into a global demand for computational power, enabling higher GPU utilization and passive revenue streams.

How Spheron Works

Spherons GPU providers are automatically matched with end users via an Eigenlayer AVS-based matching engine, which allocates resources based on:

Region/Availability Zone : Matches based on geographical proximity to reduce latency and comply with local data laws.
Price Delta : Aligns user budgets with provider bids for cost-efficiency.
Uptime/Availability : Prefers providers with reliable service histories.
Reputation : Considers providers' past performance and standing within the network.
Resource Availability : Matches based on providers' current capacity to meet demand.
Slash Rate : Takes into account any penalties providers have received for contract breaches.
Token Stakes : Favors providers who invest more in the network, enhancing their chances of selection.
Randomness : Adds unpredictability to the selection process to prevent implicit biases

In addition to the above, Spherons platform ensures that all interactions are secured with advanced smart contracts, guaranteeing transaction transparency and timely payments. In order to streamline this entire process, Spheron utilizes Layer 2 scaling solutions such as the Arbitrum Orbit stack to significantly reduce operational costs and increase transaction speed, ultimately impacting your earnings.

A Place for Every Provider

Spheron recognizes the diversity in the GPU provider community and offers structured tiers catering to various resource availability levels. If you're considering contributing your GPU resources, here's a breakdown of the provider tiers that might suit your setup:

Entry Tier : Ideal if you have GPUs priced below $1,000, suitable for basic model inferencing, offering modest performance.
Low Tier : Best for GPUs under $2,000, fit for less demanding machine learning tasks and inferencing.
Medium Tier : Perfect for GPUs under $5,000, commonly used in commercial applications for distributed training and model inferencing.
High Tier : If you own premium GPUs over $7,500, these are great for training large language models and handling other intensive tasks.
Ultra-High Tier : For those with GPUs over $15,000, designed for the most demanding tasks in training large language models and other intensive computational needs.

Each tier is structured to ensure that, regardless of the size of your operations, you can play an active role within Spherons ecosystem and earn passive income from your resource contributions.

Start Earning with Spheron

Choosing Spheron means more than just additional income; it's about becoming part of a cutting-edge technological ecosystem, reshaping how computational resources are distributed and utilized globally. The platform not only supports your business model but also contributes to a broader ecosystem that promotes innovation and development across multiple fields, including AI and machine learning.

Rather than sit on idle GPU power, youre better off leveraging your untapped resources to power a new era of innovation. Whether you're looking to make the most out of your idle GPUs or want to directly contribute to growing fields like AI and machine learning, Spheron has a place for you.

Learn more in Spherons v1 white paper.

Dive into Spheron's Whitepaper V1

SpheronStaff — Thu, 09 May 2024 15:50:48 +0000

Spheron is on track to reach multiple major milestones this year, and to give you a better sense of what's coming, we've decided to release our whitepaper V1!

This comprehensive document outlines our vision and technological foundation for decentralizing global GPU compute resources. We hope to give you an in-depth understanding of how Spheron is designed to democratize access to crucial GPU resources, lower costs, and power innovation across computation-heavy fields such as AI and machine learning.

Lets dive in.

A Blueprint for On-demand DePIN Compute

Whether you're a developer in need of computational power or a GPU owner interested in earning passive returns, Spheron has something to offer you. Our whitepaper is a cornerstone of our strategy to actualize this vision.

Key highlights include:

Ecosystem Vision and Opportunities : Learn about the transformative potential of Spheron's decentralized computing environment and how we plan on democratizing access to global GPU resources.
Key User Benefits : Unpack all the benefits Spheron offers GPU users and providers alike, including performance bonuses for GPU providers and cost advantages that surpass that of traditional service providers.
Technical Product Details : Gain insights into Spheron's technical architecture, including how the Eigenlayer AVS-based matching engine works and how the system leverages the Arbitrum Orbit stack to achieve unrivaled scalability and speed.
Future Developments : Catch a sneak peek of upcoming research priorities and potential enhancements to Spheron as we explore new avenues for increasing global GPU accessibility.

Innovate with Us

Spheron is just getting started, and your insights are welcome as we continue to refine and expand our platform. To that end, here are some ways you can get involved today:

Join the Discourse : Jump into our Discourse to discuss the whitepaper with the community and Spheron team, swap ideas, and get your questions answered.
Stay in the Loop : Join our Discord for the latest updates and behind-the-scenes looks as we gear up for the testnet launch and more big moments this year.
Test Your Smarts : Think you've got a good grasp on our tech? Take a short quiz on Galxe to win a commemorative Spheron NFT! (live starting May 14)

We are rapidly approaching the next phase of Spheron's development and will have some exciting news to share soon. In the meantime, we encourage you to review the whitepaper in full.

We hope you join us as we pave the way for a more inclusive, efficient, and scalable DePIN ecosystem, and we look forward to what comes next!

AI will inevitably become decentralized, and this is a positive development. Here's why

SpheronStaff — Fri, 12 Apr 2024 18:30:00 +0000

The world of artificial intelligence (AI) is expanding at an unprecedented rate, which can be overwhelming to comprehend. Even experts in AI find it challenging to keep up with the game-changing innovations developed every week. The pace of advancement is beyond what our human brains can fathom. While we are accustomed to linear growth, AI's growth is exponential. It takes only five years of development to reach a milestone, but it doubles its improvement in a year. We are actively participating in pushing technology forward at an incredibly fast pace that we can hardly gauge.

Most people don't understand how AI workshow it is developed, coded, trained, and "thinks"which significantly hinders building effective AI systems. This creates multiple complications when trying to develop AI models that are more advanced than what we have today. The development process for AI should involve companies competing to create increasingly capable tools. While AI has made some progress, many companies have invested heavily in the software and hardware necessary for these innovations to succeed.

The use of cloud infrastructure is finite, and with exponential development, it becomes increasingly difficult for a single company to bear the burden. Even with Google investing $10 billion in OpenAI's products, it is clear that this won't be enough in a few years. Exponential growth cannot follow the traditional model, and if AI is to continue growing at this rate, it needs a different type of infrastructure to handle it. A decentralized AI model is emerging, and it is not new but has been supercharged with the emergence of blockchain. The parallel growth of AI and blockchain is not a coincidence, and the implications and opportunities are enormous.

The fusion of decentralization and AI is inevitable and will undoubtedly accelerate the progress of AI. Several thought leaders in the field, including Stability AI's former CEO, Emad Mostaque, have also recognized this fact. Mostaque has stated that centralized AI cannot be defeated with more centralized AI. A report by TenSquared Capital highlights the pivotal role of blockchain capabilities in AI's growth. Without a doubt, there are compelling reasons why decentralized AI will outperform centralized AI.

1. Scalability

The biggest constraint for AI in the coming years will undoubtedly be scalability. As AI continues to evolve and develop new capabilities, it demands an insatiable amount of training data and model learning/development. In particular, running a Large Language Model (LLM) such as ChatGPT requires a staggering amount of hardware resources. However, as advanced as ChatGPT is, it is only a fraction of the size of the models that will be developed and trained shortly.

The more data we generate and collect, the more sophisticated AI models will become. And as AI models become more efficient and versatile, their demand will only increase. Therefore, any AI scaling effort must be decentralized to support this growth.

While decentralized processing has existed for decades, AI presents unique challenges because it requires both hardware and software to scale. The collaboration required for an AI model is far more complex than just processing data. Parallel computing must be accurately parsed out and then reintegrated into the model. Additionally, typical devices such as PCs, laptops, and phones are not equipped to run complex algorithms, which makes specific hardware ideal.

Currently, HyperCycle is the leading AI machine builder that creates AI machines for users to purchase and operate, acting as nodes in a decentralized network and sharing computational tasks under the guidance of a coordinating architecture that integrates the processed data. The modular format of this approach is perfectly suited for indefinite scalability. Moreover, the cost for clients who use the system for their AI needs will depend on the supply and demand, following the natural market model. Thus, as demand goes up, prices go up, incentivizing more nodes to be purchased and set up by users.

2. Security

It's important to note that once an AI processing infrastructure becomes decentralized, it is exposed to major security risks that can have catastrophic consequences if not addressed properly. However, with the help of blockchain technology, we can develop the necessary tools and processes to keep AI models safe and secure from bad actors. To achieve this, we can encrypt the models using various techniques, with Zero Knowledge ZK methodology being the most notable. This allows data to remain on-chain and encrypted, while still enabling interaction with other nodes through Multi-Party Computation (MPC). Decentralized AI architecture must have this critical component of secure and private encryption to enable countless use cases, from processing sensitive data such as company private or personal health records to developing proprietary models.

3. Transparency

It is imperative to ensure that all decentralized AI models are not only secure, but also transparent for certain use cases that serve the public good. In order to achieve maximum trustworthiness, full transparency must be maintained, allowing anyone to scrutinize the model's thought process, decision-making and the weights/biases used. On-chain information provides the perfect solution for transparency, as it can be posted and viewed in a completely visible manner. Moreover, the use of blockchain technology ensures that the data remains immutable, thus protecting both the data and the community that relies on certain AI use cases to be completely clear and transparent.

4. Democratization of AI

Decentralized AI uses blockchain technology to transform opaque AI systems into transparent networks, enabling trustless applications to use AI without depending on a few trusted entities. This democratization of AI development and applications is critical as AI systems progress, facilitating innovation across industries without the centralization of control that centralized AI systems impose.

5. Reduced Influence of Centralized Entities

Today, most of the AI technology is centralized in black boxes owned by a few influential organizations. This concentration of control can hinder the democratizing potential of AI, giving an unfair advantage to a handful of unchecked entities in areas such as society, finance, and creativity. However, decentralized AI can mitigate this issue by distributing control over AI technology. This prevents any single entity from imposing a monopoly on the technology and ensures that there is no single set of incentives, constraints, or goals.

6. Reduced Bias and Collective Intelligence

Decentralized AI networks utilize a variety of data inputs to minimize bias, resulting in more equitable and impartial AI-driven decisions. Furthermore, models in a decentralized network continually learn from one another, improving the overall intelligence of the network and allowing it to self-improve over time.

7. Transparency and Verifiability

Decentralized AI networks have the potential to allow open testing benchmarks and guardrails. This can provide transparency into how foundation models operate without requiring trust in a specific provider. The importance of this transparency cannot be overstated when evaluating the benefits of decentralization in AI, particularly in large foundation models like GPT-4. Understanding the inner workings of such models is impractical, but transparency can help us assess their strengths more effectively.

8. Addressing the Gap in AI Development :

The dominant position held by major AI providers leads to a significant disadvantage for their competitors. However, an efficient decentralized network model could establish an environment where multiple parties work together to enhance model quality, making knowledge more attainable and sharing benefits more accessible.

Closing Thoughts

As AI models become increasingly complex and ubiquitous in our daily lives, decentralized AI is the only way forward for AI development, training, and deployment of models. The decentralized nature is perfectly suited to AI's exponential growth, and platforms like HyperCycle are already developing the necessary hardware and software to deploy AI that is scalable, secure, and transparent when necessary. The combination of AI and blockchain is truly unparalleled, and will enable our AI journey to continue growing stronger as we move into the future.

How AI and ML Drive the Global GPU Market

SpheronStaff — Wed, 10 Apr 2024 18:30:00 +0000

An electronic circuit/chip, a graphics processing unit (GPU), is designed to manipulate and alter memory to accelerate image creation in a frame buffer intended for display output. GPUs are utilized in various platforms, such as game consoles, personal computers, workstations, mobile devices, electronic equipment, and embedded systems.

As per the Indian Electrical Equipment Industry Mission Plan 2012-2022, the Indian government aims to transform the country into an electrical equipment manufacturer and achieve a productivity of USD 100 billion by matching exports and imports.

With the widespread use of computing devices, such as laptops and personal computers (PCs), and a surge in investment in the electronics and automobile sector, the GPU market has witnessed significant growth in recent years. Additionally, the expansion of technologies like AI, the trend of real-time analysis, and the increasing demand for high-end graphics and computing applications are expected to drive further growth in the graphics processing unit market over the forecast period.

Graphic Processing Unit (GPU) Market size was valued at USD 33.47 Billion in 2021 and is projected to reach USD 477.37 Billion by 2030 , growing at a CAGR of 33.3% from 2022 to 2030.

Graphics Processing Unit (GPU) Market Poised for Growth

The Graphics Processing Unit (GPU) Market is projected to achieve a Compound Annual Growth Rate (CAGR) of 33.3% during the forecast period. The market was valued at USD 37.9 billion last year and is anticipated to reach USD 206.95 billion within the next five years.
In recent years, there has been a surge in demand for high-end personal computing devices and gaming consoles, driving the need for graphics add-in boards, which are crucial components of the final product. The market's growth has been fueled by the widespread adoption of computing devices such as personal computers (PCs) and laptops globally, alongside increased investments in the gaming industry.
The expansion of technologies like Artificial Intelligence (AI) and the trend towards real-time analysis are broadening the scope of GPU technology over the forecast period, driven by the demand for high graphics and computing applications. The gaming industry stands out as a significant driver for the GPU market, with increasing investments and advancements in game development leading to a higher demand for high-graphics capabilities.
Emerging technologies such as Augmented Reality (AR), Virtual Reality (VR), and AI are driving further demand for GPUs due to their requirement for high-speed analysis, making GPUs an ideal choice. Despite AI chips surpassing GPUs in performance and energy efficiency, GPUs remain integral to high-performance computing due to their solid general-purpose computing capability.
Manufacturing standalone GPU chips entails high costs and necessitates high-end machinery despite the cost-effectiveness of raw materials, leading companies to make significant initial investments in testing and manufacturing facilities.
The COVID-19 pandemic initially disrupted the GPU industry's supply chain but subsequently led to increased consumer demand in specific segments, supporting the growth of GPU technology. In April of this year, Nvidia utilized high-performance computing with NVIDIA GPUs to analyze data from the Hubble telescope, enhancing understanding of various planets.

Servers Application Segment is Expected to Hold Significant Market Share

The server application segment is projected to capture a substantial share of the GPU market due to the increasing adoption of cloud services across different industries. As an example, KDDI, a major Japanese telecommunications company, collaborated with NVIDIA to provide its GeForce Now game-streaming service to customers through a low-latency broadband and 5G network, utilizing NVIDIA's RTX gaming servers installed in a new data center in Tokyo.
GPU-as-a-Service (GPUaaS) has gained popularity for several applications, such as training multilingual AI speech engines and detecting early signs of diabetic retinopathy. Modern GPUaaS offers faster processing at lower costs without requiring capital expenditures than conventional general-purpose processors.
In addition, there is growing interest in the Indian market from GPU vendors, with Acer launching new NVIDIA Tesla GPU-powered servers in India. These servers feature up to eight NVIDIA Tesla V100 32GB SXM2 GPU accelerators and PCIe slots for high-speed connectivity.
Advancements in high-performance computing (HPC) technology have opened up opportunities for GPU manufacturers. For instance, scientists recently employed a supercomputer equipped with NVIDIA GPUs to identify patterns in Hubble data related to 25 exoplanets, enhancing our comprehension of their fiery atmospheres. Furthermore, according to Steam, approximately 91.22% of users utilize DirectX 12 GPU graphics cards as of August 2022.

The APAC region is expected to drive market growth during the forecasted period

The graphics processing unit market is expected to dominate the Asia Pacific region during the forecast period. The region is witnessing a surge in demand for electronic devices such as smartphones and laptops, particularly in countries such as China and India, thereby driving the regional economy. According to the International Data Corporation, India delivered 14.8 million units of personal computers (PCs) in calendar 2021, reflecting a 44.5% YoY increase. The country's PC industry witnessed successful shipment growth, including desktops, notebooks, and workstations.

Furthermore, the increasing expenditure on data centers and research centers across the Asia Pacific region is projected to boost the demand for graphics processing units. The Ministry of Finance announced in February 2023 that three centers of excellence for artificial intelligence (AI) will be established under the National Data Governance Policy, providing access to anonymized data in these data centers.

The demand for graphics processing units in the Asia Pacific region will increase dramatically, as evidenced by the government's announcement to construct one of two data center clusters in the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone in Shanghai, Suzhou (Jiangsu Province), and Jiaxing (Zhejiang Province). As more countries in the Asia Pacific region invest in data centers and the demand for electronic devices continues to soar, the graphics processing unit market is expected to experience robust growth.

For more insights on the market share of various regions, Download PDF Sample now!

According to estimates, APAC is set to contribute 42% to the global market growth and forecasting during the forecast period. The US and France are the only two countries that have come close to contributing to market growth. Technavios analysts have meticulously elaborated on the regional trends and drivers shaping the market during the forecast period. APAC is a clear leader in the global market, thanks to various factors such as technological advancements, growing demand for gaming and virtual reality applications, and significant regional market players.

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What sets APAC apart is the presence of major players such as NVIDIA, AMD, and Intel, who play a crucial role in influencing the industry. These companies deliver cutting-edge solutions to cater to the growing demand of various regional industries. Furthermore, the region boasts a well-developed electronics manufacturing industry that allows companies to produce at competitive prices. As a result, manufacturers are flocking to establish their production facilities in APAC, further bolstering the market dominance of the region.

GPU Market Customer Landscape

The market report includes the adoption lifecycle of the market, covering from the innovators stage to the laggards stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their growth strategies.

Market Overview

To learn more about this report, View the Report Sample

Current Trends and Insights

The adoption of AI and ML is an undeniable trend shaping market growth. The market is currently experiencing a major shift towards implementing these technologies. This increase can be attributed solely to the numerous benefits that AI and ML bring, making them highly sought-after technologies. Their ability to significantly enhance the overall processing performance is one of the primary reasons behind the growing adoption of AI and ML in the market.

AI and ML algorithms can process large amounts of data at lightning-fast speeds, allowing for real-time visual computing and rendering. This is especially critical in the gaming, VR, and AR industries, where high-quality graphics and smooth performance are paramount.

Furthermore, the growing adoption of AI and ML is also driven by the remarkable advancements in hardware and software. With powerful tools and libraries to harness the capabilities of AI and ML tasks, software frameworks such as TensorFlow, PyTorch, and CUDA provide developers with many options to explore. Therefore, these factors are expected to significantly increase the demand for AI and ML in the market, which will undoubtedly show positive results during the forecast period.

Crypto AI projects would need to purchase chips worth their entire market capitalization to achieve their goals.

Hundreds of graphics processing units (GPUs) will be required for mainstream text-to-video generation. This number is currently more than what Microsoft, Meta, and Google combined possess. The first demo of OpenAI's text-to-video generator Sora amazed the world, leading to a surge in interest in Artificial Intelligence (AI) tokens. As a result, several crypto AI projects emerged, promising to deliver text-to-video and text-to-image generation. According to CoinGecko data, the AI token category has a $25 billion market cap. Behind the promise of AI-generated videos are armies of Graphics Processing Units (GPUs), the processors from the likes of Nvidia and AMD, which make the AI revolution possible thanks to their ability to compute large volumes of data. But how many GPUs will it take to make AI-generated video mainstream? The answer is more than what major big tech companies had in their arsenal in 2023.

ChatGPT, hailed by Bill Gates as as revolutionary as mobile phones and the internet, has become hugely popular worldwide. This intelligent chatbot heavily relies on high-performance GPU chips to train models, as its underlying technology is AI natural language processing. Therefore, the recent popularity of ChatGPT has led to a surge in market demand and market heat for products from NVIDIA of which have GPU computing power as their core. Specifically, after NVIDIAs stock price dropped nearly 60% in the first half of 2022, its market value rose 85% in the past three months of 2023, thanks to ChatGPT.

According to data, NVIDIA's market cap was $2.265 trillion as of April 2024, making it the world's third-most valuable company by market cap.

How did Nvidia generate such high revenue in the fiscal year 2024?

Nvidia's success in the past year was inevitable, given the company's years of hard work in building a comprehensive and integrated stack of chips, systems, software and services for accelerated computing, with a primary focus on data centers, cloud computing and edge computing. The company found itself at the center of a massive demand cycle due to the hype around generative AI, which was mainly kicked off by the late 2022 arrival of OpenAI's ChatGPT, a chatbot powered by a large language model that can understand complex prompts and respond with an array of detailed answers.

The tech industry found more promise than concern with the capabilities of ChatGPT and other generative AI applications that had emerged in 2022, like the DALL-E 2 and Stable Diffusion text-to-image models. Many of these models and applications had been trained and developed using Nvidia GPUs because the chips are far faster at computing such large amounts of data than CPUs ever could.

The enormous potential of these generative AI applications kicked off a massive wave of new investments in AI capabilities by companies of all sizes, from venture-backed startups to cloud service providers and consumer tech companies, like Amazon Web Services and Meta.

Nvidia's data center GPU, the H100, with a new feature called the Transformer Engine, was designed to speed up the training of transformer models by as many as six times compared to the previous-generation A100. Among the transformer models that benefitted from the H100's Transformer Engine was GPT-3.5, short for Generative Pre-trained Transformer 3.5. This is OpenAI's large language model that exclusively powered ChatGPT before the introduction of the more capable GPT-4.

Nvidia's CUDA parallel computing platform and programming model allowed the company's GPUs to run HPC workloads faster than CPUs by breaking them down into smaller tasks and processing those tasks simultaneously. Since its introduction in 2007, CUDA has dominated the landscape of software that benefits accelerated computing.

Over the last several years, Nvidia's stack has grown to include CPUs, SmartNICs and data processing units, high-speed networking components, pre-integrated servers and server clusters as well as a variety of software and services, which includes everything from software development kits and open-source libraries to orchestration platforms and pretrained models.

Nvidia's data center business grew by 217% to $47.5 billion in its 2024 fiscal year, which represented 78% of total revenue. This was mainly supported by a 244% increase in data center compute sales, with high GPU demand driven mainly by the development of generative AI and large language models. Cloud service providers and consumer internet companies contributed a substantial portion of Nvidia's data center revenue, with the former group representing roughly half and then more than a half in the third and fourth quarters, respectively.

Nvidia's CEO Jensen Huang stated that this represents the industry's continuing transition from general-purpose computing, where CPUs were the primary engines, to accelerated computing, where GPUs and other kinds of powerful chips are needed to provide the right combination of performance and efficiency for demanding applications.

GPU sales saw 32% year-over-year increase in Q4 AMD's market share rises to 19%

According to a report from Jon Peddie Research, the discrete graphics card market saw a remarkable recovery in Q4 of last year, with shipments rising by 6.8% compared to Q3 2023 and a staggering 32% compared to Q4 2022. Both Nvidia and AMD experienced growth in sales quarter-to-quarter and year-over-year, but AMD's growth was considerably higher, increasing its market share to 19%. The CPU market is also recovering, with shipments returning to 2022 levels.

Despite the COVID-19 pandemic driving up the sales of discrete GPUs in 2020 and 2021, shipments declined sharply in 2022, probably due to a globally weakening economy that led some countries into recession. However, the graphics card sales saw a significant recovery in 2023, and Q4 continued this trend with a 6.8% gain in shipments.

As a result of these consecutive quarterly increases in shipments, Q4 of 2023 witnessed 32% more discrete GPUs shipped than in Q4 of 2022. The difference is almost 40% compared to Q1 of 2023. Though Jon Peddie Research didn't elaborate much on the highlights of its GPU market report, rising desktop gaming GPU sales played a significant role in the increased sales seen in Q3, and perhaps that continued into Q4.

The CPU shipments are similarly recovering rapidly, with Q4 outperforming three of 2022's quarters. Though the CPU market has recovered better than the discrete graphics card market, it's still insufficient to put it on par with 2020 and 2021, which saw some of the highest shipments on record.

Intel's shipments were mostly flat among the three major GPU vendors, while Nvidia and AMD saw quarterly and yearly growth. Nvidia's shipments went up 4.7% from Q3 and 22.3% from Q4 of 2022, but that's nothing compared to AMD's gains of 17% quarter-to-quarter and 117% year-over-year. AMD has fueled a decent chunk of the GPU market's recovery in Q4 and 2023.

As AMD grew faster than Nvidia, its market share increased to 19% - up from 17% in Q3. AMD's market share is also up 7% compared to Q4 of 2022, which had been a terrible year for Radeon graphics cards. According to Jon Peddie Research, AMD was only at 10% market share in Q3 of 2022, likely the lowest since buying ATI and its Radeon graphics business in 2006. Although 17% is still relatively low, it's undoubtedly an improvement.

Jon Peddie, the founder of the eponymous research firm, states that GPU customers are "pretty damn happy and vote with their dollars," which explains the increasing shipments. Though the market seems to be "entering a golden age," Peddie warns us not to get ahead of ourselves and overreact, as we did in the past with crypto and Covid.

Need Army of 720,000 Nvidia H100 GPUs

According to the research report by Factorial Funds, it is estimated that a whopping 720,000 high-end Nvidia H100 GPUs will be required to support the creator community of TikTok and YouTube. In addition, Sora, as Factorial Funds has pointed out, requires up to 10,500 powerful GPUs for a month to train and can generate only about 5 minutes of video per hour per GPU for inference. As shown in the chart below, training this requires significantly more computing power than GPT4 or still image generation. However, with widespread adoption, inference will surpass training in compute usage. This implies that the computer power needed to create new videos (inference) will become greater than the power needed to train the AI model initially as more people and companies start using AI models like Sora to generate videos.

To put things in perspective, it is worth noting that Nvidia shipped 550,000 of the H100 GPUs in 2023. Data from Statista shows that the twelve largest customers using Nvidia's H100 GPUs collectively have 650,000 of the cards, and the two largestMeta and Microsofthave 300,000 between them. Assuming a cost of $30,000 per card, it would take $21.6 billion to bring Sora's dreams of AI-generated text-to-video mainstream, which is nearly the entire market cap of AI tokens.

It is important to note that even if you have the financial resources to acquire all the GPUs needed to support the TikTok and YouTube creator community, physically getting your hands on them could be challenging.

Nvidia isn't the only game in town

It's crucial to acknowledge that despite its reputation, Nvidia isn't the only player in the AI game. AMD, its long-time chip rival, offers competing products that investors have rewarded handsomely, driving its stock from the $2 range in 2012 to over $170 today.

While Render (RNDR) and Akash Network (AKT) offer distributed GPU computing, it's worth noting that most of the GPUs on these networks are retail-grade gaming GPUs, which are significantly less powerful than Nvidia's server-grade H100 or AMD's competition.

The text-to-video promise Sora and other protocols pledge will require a monumental hardware lift. Although it's an exciting premise that could revolutionize Hollywood's creative workflow, it's unlikely to become mainstream anytime soon. Simply put, we'll need more chips.

How DePIN Democratizes Innovation, Overcome GPU Shortages and Establish Trust

The GPU shortage will drive the mainstream adoption of Web3 infrastructure. High-end graphics processing units (GPUs) such as the NVIDIA A100s and H100s are essential for training artificial intelligence (AI) models. However, these GPUs are extremely expensive, have limited availability, and are only required for a short period for each model. This combination makes it impossible for many startups to own them.

Decentralized physical infrastructure networks (DePINs) provide the solution, particularly compute and storage networks. These networks will revolutionize AI and Web3 for two primary reasons: access and safety.

The emergence of AI will be the turning point for Web3 infrastructure since these protocols offer solutions to the data challenges and GPU shortages faced by AI startups. DePINs will transform AI development, making high-cost resources accessible to smaller players, democratizing AI innovation, and allowing hardware owners to generate passive income.

DePIN provides the antidote for trustworthy code and the physical infrastructure networks on which it runs. It's important to note that if startup founders build their apps on infrastructure controlled by internet Goliaths like Amazon Web Services (AWS), it becomes challenging for startups to compete since they have to pay a significant portion of their revenues to their competitors and take on counterparty risk.

Top 15 Open-Source LLMs for 2024 and Their Uses

SpheronStaff — Tue, 09 Apr 2024 18:30:00 +0000

The current revolution in generative AI owes its success to the large language models (LLMs). These AI systems, built on powerful neural architecture, are used to model and process human language and are the foundation of popular chatbots like ChatGPT and Google Bard. However, many of these models are proprietary and owned by Big Tech companies, which limits their accessibility and transparency. Thankfully, the open-source community has risen to the occasion by creating open-source LLMs that promise to make the field of generative AI more accessible, transparent, and innovative. In this article, we'll explore the top open-source LLMs available in 2024, which have already achieved significant milestones in just one year since the launch of ChatGPT and the popularization of (proprietary) LLMs.

This article will explore the top open-source LLMs available in 2024. In just one year since the launch of ChatGPT and the popularization of (proprietary) LLMs, these LLMs have already achieved significant milestones.

Benefits of Using Open-Source LLMs

Choosing open-source LLMs has many benefits over proprietary LLMs, both short-term and long-term. Below is a list of the most compelling reasons:

Improved Data Security and Privacy: Open-source LLMs give companies complete control over personal data protection, eliminating the risk of data breaches and unauthorized access from third parties.
Cost Savings and Reduced Vendor Dependency: Unlike proprietary LLMs, open-source options do not require licensing fees, offering significant cost savings for businesses, particularly small and medium enterprises. However, keep in mind that running LLMs still demands substantial computational resources.
Code Transparency and Model Customization: Access to the source code, architecture, training data, and mechanisms of open-source LLMs enables better understanding, scrutiny, and tailored adjustments according to specific business needs.
Active Community Support and Innovation: Through global developer engagement, Open-source LLMs promote collaboration, innovation, and improved performance. They help address biases and enhance accuracy while encouraging eco-friendly advancements in AI.
Environmental Footprint Awareness: Unlike proprietary LLMs, open-source alternatives provide greater transparency regarding resource usage and environmental impact, paving the way for sustainable AI practices and innovations.

Top Open-Source Large Language Models For 2024

1. OPT-175B

The year 2022 saw the release of Open Pre-trained Transformers Language Models (OPT), a significant step towards Meta's mission to democratize the LLM industry through open-source technology.

OPT is a set of decoder-only pre-trained transformers ranging from 125M to 175B parameters. OPT-175B is one of the most advanced open-source LLMs available today, comparable in performance to GPT-3. Both the source code and pre-trained models are accessible to the public.

However, suppose you plan to develop an AI-driven company with LLMs. In that case, you should look for alternatives, as OPT-175B is released under a non-commercial license that only permits the use of the model for research purposes.

2. XGen-7B

Many companies are joining the LLM race, and Salesforce recently joined with its XGen-7B LLM in July 2023. Unlike most open-source LLMs, which provide limited information with short prompts, XGen-7B aims to provide a tool that supports longer context windows. The most advanced version of XGen (XGen-7B-8K-base) allows for an 8K context window, which includes the cumulative size of the input and output text.

Efficiency is also a top priority in XGen, which uses only 7B parameters for training, much less than other powerful open-source LLMs like LLaMA 2 or Falcon. Despite its smaller size, XGen can still deliver excellent results. The model is available for commercial and research purposes, except for the XGen-7B-{4K,8K}-inst variant, which is trained on instructional data and RLHF and is released under a noncommercial license.

3. GPT-NeoX and GPT-J

GPT-NeoX and GPT-J are two open-source alternatives to GPT, developed by researchers from EleutherAI, a non-profit AI research lab. Both are language models that can perform various natural language processing tasks, including text generation, sentiment analysis, research, and marketing campaign development.

GPT-NeoX has 20 billion parameters, while GPT-J has 6 billion parameters. Although most advanced language models can be trained with over 100 billion parameters, these two LLMs can still deliver highly accurate results.

They have been trained with 22 high-quality datasets from diverse sources, making them suitable for use in multiple domains and various use cases. Unlike GPT-3, GPT-NeoX and GPT-J haven't been trained with RLHF. The good news is that both LLMs are available for free through the NLP Cloud API.

4. Vicuna 13-B

Vicuna-13B is an open-source conversational model based on the LLaMa 13 B model. It has been fine-tuned using user-shared conversations gathered from ShareGPT. As an intelligent chatbot, it has countless applications in various industries, such as customer service, healthcare, education, finance, and travel/hospitality.

A preliminary evaluation using GPT-4 as a judge showed that Vicuna-13B achieved more than 90% of ChatGPT and Google Bard quality. It outperformed other models like LLaMa and Alpaca in more than 90% of cases.

5. LLaMA 2

Many leading players in the Large Language Model (LLM) industry have chosen to develop their models privately. However, Meta is breaking this trend by making its LLM available to the public. Meta recently released its open-source LLaMA (Large Language Model Meta AI) and its improved version, LLaMA 2, which is a significant move in the market.

LLaMA 2 is a pre-trained generative text model with 7 to 70 billion parameters designed for research and commercial use. It has been fine-tuned using Reinforcement Learning from Human Feedback (RLHF). It is a versatile text model that can be used as a chatbot and adapted for various natural language generation tasks, such as programming. Meta has already launched two versions of LLaMA 2: Llama Chat and Code Llama, which are customizable and open to the public.

6. BLOOM

Launched in 2022, BLOOM is an autoregressive Language Model trained by researchers from Hugging Face and volunteers from over 70 countries. It is designed to generate coherent and accurate text from a prompt by utilizing vast amounts of text data and industrial-scale computational resources.

The release of BLOOM was a significant milestone in making generative AI more accessible. With 176 billion parameters, BLOOM is one of the most powerful open-source Language Models available, capable of providing text in 46 human languages and 13 programming languages. Transparency is a key feature of BLOOM - anyone can access the source code and training data to run, study and improve it.

BLOOM is available for free through the Hugging Face ecosystem.

7. BERT

LLM, or Language Model, is a type of neural architecture that uses a transformer. In 2017, Google researchers developed the transformer architecture in a paper called "Attention is All You Need". One of the first models to use transformers was BERT.

Google released BERT as an open-source LLM in 2018, and it quickly became one of the best-performing models for many natural language processing tasks. Because of its innovative features and open-source nature, BERT is now one of the most widely used LLMs. In 2020, Google announced that it had integrated BERT into Google Search in over 70 languages.

Today, thousands of pre-trained BERT models are available for various use cases, such as sentiment analysis, clinical note analysis, and toxic comment detection. These models are open-source and free to use.

8. Falcon 180B

The Falcon 40B has already impressed the open-source LLM community and ranked #1 on Hugging Faces leaderboard for open-source large language models. Now, the new Falcon 180B suggests that the gap between proprietary and open-source LLMs is rapidly closing.

The Technology Innovation Institute of the United Arab Emirates released Falcon 180B in September 2023. It is trained on 180 billion parameters and 3.5 trillion tokens, making it an incredibly powerful tool. Falcon 180B has already outperformed LLaMA 2 and GPT-3.5 in various NLP tasks, and Hugging Face suggests that it can rival Googles PaLM 2, the LLM that powers Google Bard.

It's worth noting that while Falcon 180B is free for commercial and research use, it requires significant computing resources to function properly.

9. Baichuan-13B

Chinas pioneering search engine company, Baichuan Inc., has unveiled an open-source large language model named Baichuan-13B, aiming to compete with OpenAI. With a model size of 13 billion parameters, it seeks to empower businesses and researchers with advanced English and Chinese AI language processing and generation capabilities.

The models pre-training dataset involves 1.3 trillion tokens. Baichuan-13B enables text generation, summarization, translation, and more tasks. This initiative comes after Baichuans success with Baichuan-7B and aligns with the companys mission to democratize generative AI for broader practical use.

10. CodeGen

The innovative CodeGen model, developed by the Salesforce AI Research team, is an exceptional creation that takes the GPT-3.5 architecture to the next level. With its impressive range of sizes, including 350 million, 2 billion, 6 billion, and a colossal 16 billion parameters, CodeGen is poised to revolutionize the software development industry.

The CodeGen training dataset is an extensive collection of code snippets from multiple programming languages and frameworks, including GitHub and Stack Overflow. With this vast dataset, CodeGen can understand programming concepts and their natural language relationships, allowing it to generate accurate and reliable code solutions from simple English prompts.

Unsurprisingly, CodeGen is garnering attention in the developer community for its potential to streamline software development processes and boost productivity. With CodeGen, developers can save time and focus on more complex tasks, confident that they have a reliable code generator.

11. T5

T5 is a pre-trained language model created by Google AI researchers. It uses the Transformer architecture to handle various natural language processing tasks through a unified "text-to-text" framework. T5 models come in 11 sizes, ranging from small to extra-large, and the largest one has 11 billion parameters.

The model was trained on the Colossal Clean Crawled Corpus (C4) dataset, which includes English, German, French, and Romanian languages. T5 redefines tasks by transforming them into a text-to-text format. This approach facilitates tasks such as translation, summarization, classification, and more by treating each task as a text-generation problem.T5, or Text-To-Text Transfer Transformer, is a versatile pre-trained language model developed by researchers at Google AI. Its based on the Transformer architecture and designed to handle a wide range of natural language processing tasks through a unified text-to-text framework. With 11 different sizes, T5s models vary from small to extra-large, with the largest having 11 billion parameters.

The models training was conducted on the Colossal Clean Crawled Corpus (C4) dataset, encompassing English, German, French, and Romanian languages. T5 redefines tasks by casting them into a text-to-text format, facilitating results like translation, summarization, classification, and more by treating each task as a text-generation problem.

12. MPT-30B

MosaicML is a leading AI research organization that has developed MPT-30B, an innovative open-source language model. With 30 billion parameters, MPT-30B is built on GPT architecture, which has been refined to provide better performance.

MPT-30B has been trained using a unique approach that involves a "mosaic" of data, including 1 trillion tokens of English text and code. This approach combines supervised, unsupervised, and reinforcement learning to provide a comprehensive learning experience.

MPT-30B has commercial applications that include content creation, code generation, and more. MosaicML is committed to open-source innovation, which empowers developers and enterprises to leverage MPT-30B's capabilities for diverse linguistic tasks.

13. Dolly 2.0

Dolly 2.0 is a new AI-powered language generation tool developed by LLM as an alternative to commercial offerings such as ChatGPT. Databricks, a well-known player in the field of AI, created Dolly 2.0, which represents a significant leap in language generation technology. Dolly 2.0 has a 12 billion parameter count and was trained on a dataset called databricks-dolly-15k, which contains 15,000 human-generated prompt and response pairs.

Dolly 2.0 is built on a GPT-3.5 architecture and has been trained on various datasets, which enables it to understand and generate high-quality text. It uses a two-step training process: first, it undergoes pre-training on extensive text corpora, and then it engages in fine-tuning through a pioneering "instruction tuning" approach. Dolly 2.0's release marks a new era for open-source LLM, providing a commercially viable alternative to proprietary models.

14. Platypus 2

Platypus 2 is a powerful player among large language models (LLMs), designed by Cole Hunter and Ariel Lee. With a model size of 70 billion parameters, Platypus 2 has taken the lead on Hugging Face's Open LLM leaderboard. The developers meticulously trained Platypus 2 on the Open-Platypus dataset, consisting of tens of thousands of finely tuned and merged LLMs.

Platypus 2 is built upon LLaMA and LLaMa 2 transformer architectures, combining the efficiency of Qlora and LLaMA 2. Its ability to generate coherent and contextually rich content across various domains sets it apart. Its substantial parameter size and ability to generate high-quality text show its pivotal role in the future of AI-driven applications, from natural language understanding to high-quality content creation.

15. Stable Beluga 2

Stable Beluga 2 is an auto-regressive LLM derived from the LLamA-2 model developed by Meta AI. It is a language model created by Stability AI that can efficiently handle complex language tasks with a high level of accuracy and understanding.

Stable Beluga 2 has been trained on a diverse and internal Orca-style dataset. The model leverages Supervised Fine Tuning (SFT) to improve its performance. This process involves exposing the model to a large corpus of carefully curated examples and guiding it toward better predictions. As a result, it increases its precision and versatility. Additionally, this process enables the model to comprehend context, generate coherent text, and provide valuable insights across numerous apps, including text generation, summarization, and more.

Choosing the Right Open-Source LLM for Your Needs

The realm of open-source LLM (Learning Management Systems) is rapidly expanding. There are currently more open-source LLMs than proprietary ones, and the performance gap between them may be bridged soon as developers worldwide collaborate to upgrade the current LLMs and design more optimized ones.

In this vibrant and exciting context, choosing the right open-source LLM for your purposes may be daunting. To help you out, here is a list of factors you should consider before opting for a specific open-source LLM:

What is your goal for using the LLM? While open-source LLMs are widely available, some may only be suitable for research.
Is an LLM necessary for your project? If not, it might be better to avoid using one to save time and resources.
How important is accuracy for your project? Generally, larger LLMs tend to be more accurate, so consider this when deciding.
How much are you willing to spend on the LLM? Larger models require more resources to train and run, which can add up quickly.
Can a preexisting, pre-trained LLM meet your needs? Many open-source LLMs have already been trained for specific tasks, so it might make sense to use one of those instead of building and training your own from scratch.

Conclusion

Open-source LLMs (language model frameworks) are becoming increasingly popular, which is an exciting development. These powerful tools are evolving rapidly, and it seems like the generative AI space won't be monopolized by only the big players who can afford it.

Currently, there are 15 open-source LLMs available, but the number is much higher and growing quickly.

List of 16 Decentralized Computing Tools (2024)

SpheronStaff — Sat, 06 Apr 2024 18:30:00 +0000

The world is becoming increasingly digitized, mainly due to the need for processing power and computing resources. This trend has been even more pronounced since the COVID-19 pandemic, which has led to a rise in remote work. Companies and individuals are turning to cloud computing services to comply with social distancing measures, reduce office maintenance, and maintain productivity. However, traditional cloud services have security vulnerabilities and high costs, primarily because of market concentration among major providers like Amazon, Google, Microsoft, and Alibaba.

Decentralized or peer-to-peer cloud computing models have emerged to address these issues. These solutions use nodal blockchain-based networks to enhance security and reduce censorship. Despite sharing a common ideology, these solutions differ in their approaches. In this article, we will explore the leading decentralized computing initiatives, examining their distinct features, benefits, and how they advance the cause of accessible computing resources in today's digital age.

1. Akash Network

Greg Osuri founded Akash Network, an open-source, decentralized cloud computing platform. Known as the Airbnb for cloud computing, Akash is an open Supercloud network that makes it easy to scale and access services around the world rapidly. It uses a "reverse auction" system, where customers submit their desired price and providers compete for the business. According to Akash, this often results in prices that are up to 85% lower than those of other cloud systems.

Akash is owned and managed by its community. It is a free public service, and the source code that powers it is available to everyone. It is built on dependable technologies like Kubernetes and Cosmos, and the community oversees all aspects of Akash including decisions about what new features should be implemented.

2. Spheron Network

Spheron Network is a decentralized computing platform that strives for fairness, making distributed resources accessible to all. Spheron Compute presents a robust and cost-effective alternative to centralized cloud services, priced at just one-third of the traditional cost. The goal is to democratize public cloud access, offering a more sustainable model for computing. The Spheron platform allows organizations and developers to deploy, run, and scale based on their needs, free from the limitations of centralized cloud environments.

Spheron satisfies compute requirements: private images, auto-scale instances, Scale on demand, Real-time instance metrics, Faster GPUs, Free Bandwidths, Terraform Providers and SDKs, Instance health checks, activity, shell access, and more. Spheron provides add-on storage solutions through its global CDN for long-term data storage and edge bandwidth acceleration. With Spheron, you can easily set up your nodes in just a few minutes and enjoy low maintenance and operations costs and a great developer experience.

This results in significant customer benefits, simplifying access to a wider range of powerful computing and distributed resources at the edge, and enhancing availability, proximity, and cost efficiency.

3. DeNet Storage

Launched in 2017, DeNet is a decentralized storage protocol allowing the utilization of global spare storage, thereby eliminating centralized data centers. Built on blockchain and Web3 principles, it guarantees unmatched file security; files are fragmented, encrypted, and can only be decrypted by the owner. Users enjoy enhanced privacy, as personal data isn't required, and files are stored in a resilient, decentralized network.

4. Media Network

Media Network is a content delivery network that hopes to provide a marketplace for decentralized, peer-to-peer bandwidth. Users can utilize Media Networks services to hire bandwidth-on-demand from decentralized providers. Developers can earn $MEDIA rewards for contributing their bandwidth to the network. The protocol allows network participants to serve content without introducing trust assumptions or pre-authentication requirements.

5. ChainJet

ChainJet provides an extensive collection of well-tested automation modules for web3 protocols, dapps, and developer tools. Users can utilize this collection of automation modules to reduce the time it takes for them to do certain repetitive processes or workflows. Developers can create their own modules and add them to the collection for other users to take advantage of. ChainJet has 8 blockchains supported and integrates with hundreds of population dapps and web3 protocols.

6. Internet Computer

The Internet Computer blockchain incorporates a radical rethink of blockchain design powered by innovations in cryptography. It provides the first World Computer blockchain that can be used to build almost any online system or service, including demanding web social media, without the need for traditional IT such as cloud computing services. As such, it can enable full end-to-end decentralization.

7. Filecoin

The project was first described in 2014 as an incentive layer for the Interplanetary File System (IPFS). In this peer-to-peer storage network, users pay for data storage and distribution services in $FIL. Filecoin is an open protocol backed by a blockchain that records commitments made by the networks participants, with transactions made using FIL, the blockchains native currency. The blockchain is based on both proof-of-replication and proof-of-spacetime.

Filecoin is open-source and decentralized, which means that all governance is in the hands of the community. On the Filecoin platform, developers can create cloud file storage services like Dropbox or iCloud. Anyone can join Filecoin and start storing their data or earn money by providing space for someone else's funds. The creators of Filecoin opted for their blockchain technology to run the network and their token with their consensus.

8. Render Network

RenderToken (RNDR) is a distributed GPU rendering network built on top of the Ethereum blockchain. It aims to connect artists and studios needing GPU compute power with mining partners willing to rent their GPU capabilities. OTOY, Inc. conceived it in 2009.

CEO Jules Urbach and launched in 2017, RNDR held its first public token sale in October of that same year, followed by a private sale period lasting from January 2018 May 2018, wherein a total of 117,843,239 RNDR were sold for 1 RNDR = $0.25 USD equivalent of the token. During the private sale period, early adopters were onboarded onto the RNDR Beta Testnet, where beta node operators and artists worked collaboratively with the RNDR team to build and test the network until its public launch on April 27th, 2020.

9. Bittensor

Bittensor is an open-source protocol that powers a decentralized, blockchain-based machine-learning network. Machine learning models train collaboratively and are rewarded in TAO according to the informational value they offer the collective. TAO also grants external access, allowing users to extract information from the network while tuning its activities to their needs.

10. Theta Network

Theta is a "dual network" consisting of two complementary subsystems, the Theta Blockchain and the Theta Edge Network.Theta proof-of-stake blockchain provides payment, reward, staking, and smart contract capabilities, while the Edge Network is responsible for the computing, storage, and delivery of video streams, AI tasks, and other scientific, simulation, and financial modeling use cases. There are two native cryptocurrencies on Theta blockchain: THETA, the staking and governance token, and TFUEL, which are used as gas for all transactions and on-chain smart contract interactions. The next-generation Edge Network, Theta EdgeCloud, is the first hybrid cloud computing platform built on a fully distributed architecture, set to launch later in 2024.

11. Arweave

Arweave is a decentralized storage network that seeks to offer a platform for indefinite data storage. Describing itself as "a collectively owned hard drive that never forgets," the network primarily hosts "the permaweb" a permanent, decentralized web with a number of community-driven applications and platforms.

12. MultiversX

MultiversX is a blockchain protocol that offers true horizontal scalability by using all aspects of sharding (Network, Transaction, & State). The project describes itself as a technology ecosystem for the new internet, which includes decentralized finance, real-world assets, and the Metaverse. Its smart contracts execution platform is reportedly capable of up to 100,000 transactions per second, 6-second latency, and a $0.002 transaction cost.

MultiversX is governed and secured through the EGLD token. EGLD, or Electronic Gold, is MultiversX's native token. It acts as a store of value currency to pay for network usage. The coin also serves as a medium of exchange between platform users and validators. Users pay transaction fees in EGLD, and validators participate in the consensus process.

13. BitTorrent

BitTorrent is a popular peer-to-peer (P2P) file-sharing and torrent platform which has become increasingly decentralized in recent years.

Originally released in July 2001, BitTorrent was purchased by blockchain platform TRON in July 2018.

Since its acquisition, BitTorrent has added various new tools, with a dedicated native cryptocurrency token, BTT, released in February 2019. BTT was launched on TRONs own blockchain, using its TRC-10 standard.

According to its official literature, BitTorrent is currently the largest decentralized P2P communications protocol in the world.

14. Helium

Helium (HNT) is a decentralized blockchain-powered network for Internet of Things (IoT) devices.

Launched in July 2019, the Helium mainnet allows low-powered wireless devices to communicate with each other and send data across its network of nodes.

Nodes come in the form of so-called Hotspots, a combination of a wireless gateway and a blockchain mining device. Users who operate nodes thus mine and earn rewards in Heliums native cryptocurrency token, HNT.

Heliums goal is to prepare IoT communication for the future, identifying inadequacies in current infrastructure from its birth in 2013.

15. AIOZ Network

AIOZ Network is a DePIN for Web3 AI, Storage, and Streaming. AIOZ empowers a faster, more secure, and decentralized future.

Powered by a global network of DePINs, AIOZ rewards you for sharing your computational resources for storing, transcoding, and streaming digital media content and powering decentralized AI computation.

16. IoTeX

Starting as an open-source project in 2017, IoTeX has built a decentralized platform to empower machines to use open economics. In this open ecosystem, people and machines can interact with guaranteed trust, free will, and properly designed economic incentives.

With a global team of over 40 research scientists and engineers, IoTeX has built their EVM-compatible blockchain from scratch using the innovative Roll-DPoS consensus and launched in 2019 April, which has been running by 100+ delegates worldwide and has processed more than 10 million transactions already. On top of the IoTeX blockchain, the team has built the essential blocks of infrastructures to connect with Ethereum, BSC, and Heco blockchains, such as ioPay wallet (https://iopay.me/) and ioTube bridge (https://iotube.org/), which serve ten thousands of users. IoTeX helps EVM-based DApps scale without concerning expensive gas fees!

Concise Overview Of Decentralized AI Powered by Web3

SpheronStaff — Fri, 05 Apr 2024 18:30:00 +0000

This article will focus on the significant contribution of Web3 technology to AI's success in today's landscape. We'll emphasize three core areas: data, computation, and algorithms. We'll delve into the pressing challenges that AI faces today and explore how Web3 technologies can provide solutions to these challenges.

We'll also present some compelling case studies that highlight innovative AI projects leveraging the existing capabilities of Web3.

The Evolution of Artificial Intelligence

Artificial intelligence (AI) has been around for a while, and it's had three big periods of improvement because of improvements in computers and the Internet.

First AI Expansion (1950s): In 1956, there was a conference about AI called the Dartmouth Conference. People got excited about AI, and early AI programs like the Logic Theorist seemed promising. But because computers weren't very powerful yet, people got disappointed when AI didn't do as much as they hoped. This disappointment led to a time when people weren't as interested in AI, called "the first AI winter."
Second AI Expansion (the 1980s): During this time, people started using expert systems, which are programs that make decisions to solve problems. People also worked on neural networks, which try to imitate how our brains work. However, these technologies had limitations, and there was a time when people lost interest in AI again, called the "second AI winter."
Third AI Expansion (Present): AI is becoming popular again. This time, we have better techniques like deep learning and machine learning, stronger computers, and lots of data to work with. Deep learning methods like CNNs and RNNs have helped AI become good at things like recognizing images, understanding language, and translating languages.

What We Can Learn From AI's History So Far

Three key factors have influenced AI's progress: computing power, data availability, and the Internet.

Computing Power - Increased computing power has allowed AI to solve more complex problems like self-driving cars and medical diagnosis.
Data Availability - The vast amount of data available has helped train and improve AI systems.
Internet - The internet has made collaboration easier for researchers worldwide, speeding up innovation.

These factors have worked together to lay the groundwork for AI's progress, and as technology continues to advance, we can expect even more exciting developments in the future.

More details at: https://ourworldindata.org/artificial-intelligence

The Formula for AI

According to the current understanding, the formula for AI is:

Computation + Data + Algorithm = AI

This means that AI needs three things to work properly:

Computation - Hardware like computers and specialized chips do complicated calculations for tasks like image recognition and natural language processing.
Data - AI learns from large amounts of data, identifying patterns and making predictions. Access to a lot of good data helps AI systems better handle real-world situations.
Algorithm - Instructions or rules that tell AI how to process data. Good algorithms help AI learn from data, make correct guesses, and adjust to new situations.

All three parts must work well together for AI to reach its full potential.

Web3 and Data

Data is essential for AI's success. High-quality data is crucial for the development of powerful and reliable AI systems.

Characteristics of Data Required for AI to Succeed

The type of data needed for AI to succeed depends on what it's being used for, but some important qualities include:

Data storage - It is an essential aspect of AI. To ensure that AI algorithms can easily process and analyze data, it should be structured in an easy-to-understand format. Structured data enables efficient model training and data analysis. Additionally, the amount of data available plays a significant role in the performance of AI models. The more data there is, the better the models will generate accurate results.
Data quality - It is crucial for the reliability and trustworthiness of AI algorithms. To achieve this, data must meet three key criteria: accuracy, completeness, and consistency. Accuracy refers to the absence of errors and inconsistencies in the data. Completeness means that data must contain all the necessary information for AI algorithms to make informed decisions. Consistency requires data to be consistent across different sources and periods to ensure its trustworthiness.".
Data readiness - It is essential to consider when developing an AI system. To ensure that the AI system works effectively in real-world situations, the data used must represent the conditions the AI system will encounter during deployment. Additionally, the data must be relevant to the task or problem that the AI system is designed to address. In supervised learning tasks, it is crucial to properly label the data to provide the necessary information for AI algorithms to learn from.
Data Availability - Collecting high-quality data can be time-consuming and expensive. Storing large amounts of data also requires significant storage capacity and infrastructure. Since AI often deals with sensitive personal information, data privacy is of the utmost importance. However, sharing data between entities can be challenging due to privacy and legal concerns.

To ensure the accuracy of AI systems, it is crucial to have data that is available, of high quality, and ready to use. Organizations should address data acquisition, storage, privacy, and sharing challenges to empower AI. By doing so, AI can provide valuable insights, make informed decisions, and effectively solve real-world problems.

How Web3 Can Improve Data Access and Quality for AI Development

In AI, big tech companies hold lots of top-notch data that is perfect for training AI models. However, this data isn't usually available to everyone because these companies keep it to themselves. This means they're the ones in charge of AI innovation.

However, big tech is not the only one making strides in AI. Plenty of researchers and developers in universities, governments, and startups are also doing great work. They face trouble getting their hands on good-quality data that's easy to access and doesn't cost a fortune to store.

In other words, the big challenge is obtaining just the right datait needs to be top-quality, the right amount, easy to obtain, and not too expensive to keep. That's where Web3 could help.

Collecting high-quality data can be complex and resource-intensive, requiring significant time, money, and specialized knowledge.
Ensuring the privacy and security of sensitive personal information is essential when collecting and using data, and organizations must comply with rigorous regulations to avoid unauthorized access or misuse.
Data integrity is critical for effectively training AI models, but it can be difficult and costly for individuals or small businesses to obtain data that meets the necessary standards of accuracy, completeness, and consistency.

There are solutions available in the form of Web3 that can assist in resolving or easing the difficulties experienced by AI researchers. The principle of decentralization at the core of Web3 has the potential to tackle multiple challenges associated with obtaining valuable datasets for AI development.

1. Decentralized Data Storage

Decentralization of data storage offers significant benefits for AI in terms of cost reduction and security.

2. Cutting Costs

Instead of storing all data in one expensive place, decentralized storage spreads it across many cheaper places. This can save money for AI companies, which usually pay a lot to store data on big servers from big tech companies.

For example, Spheron Network is a project that lets companies store data on a network of many computers instead of one big server and provides computing power to process this data. This makes it cheaper for AI companies to store data, saving them money.

3. Boosting Security

Centralized storage has a big problem: all the data is lost if the main server fails. This could happen because of power cuts, broken equipment, or hackers. Decentralized storage fixes this by storing data on many computers worldwide. If some computers fail, the others keep working, so the data stays safe.

4. Data Ownership, Quality, and Integrity

Right now, big companies usually own the data used to teach AI. This can be a problem because the people who create the data don't get anything. Sometimes, they don't even know their data is being used, which isn't fair.

Also, when AI learns from data collected by others, it might not work very well for everyone. That's because the data might not be accurate or might only represent some groups of people, which isn't fair.

Using blockchain technology, people can own their data and decide who gets to use it. This means they have control over how their data is used and can ensure it's used fairly.

Making Data Clear and Accountable with Web3

On current online platforms (Web2), there's a big problem with not being open about where data comes from and how it's used. This lack of transparency leads to some serious issues:

Data Confusion : When it's unclear where data comes from, it's easy for people to change it and spread wrong information. This can lead to bad decisions based on fake data.
Privacy Problems : Without knowing who's using our data and how our privacy can be invaded. Companies might be tracking us without us knowing, using our data for targeted ads or surveillance.
No One to Blame : If companies aren't clear about how they use data, holding them accountable for any bad practices is hard. This makes it tough to stop unfair treatment or misuse of data.
Hard to Share Data : It's tough to work together and share information without clear data rules. This can slow down progress and block new ideas and products.

Web3 technology, like blockchain, can help solve these issues. It can create a record that shows where data comes from and how it's used, making things more transparent and trustworthy.

Also, new ways of managing data together (decentralized governance) can give people more control over their data, promoting honesty and responsibility in data use.

Empowering Data Owners with Web3

In today's online world, big tech companies usually own and profit from user data, leaving the actual creators with little to no reward.

When people do want to earn money from their data, they often face complicated processes and need to deal with middlemen or complex platforms. This makes it hard for individuals to make money efficiently from their own data. By centralizing data, we limit how it can be used, holding back its full value.

Web3 steps in to change this. It offers a decentralized method that encourages open data access, cooperation, and putting users in control. By giving individuals and groups power over their data and promoting teamwork, Web3 makes it easier to earn money from data, reduce biases, and boost privacy and security.

Web3 on AI Computation

Computational capabilities play a crucial role in the advancement of AI. The effectiveness of AI algorithms and models is often determined by the underlying hardware and software infrastructure that supports them. The computational capabilities of AI systems are typically evaluated based on several key characteristics:

Processing Power: This refers to how fast the CPU, GPU, and specialized AI chips like TPUs and FPGAs can do their jobs. Each one does different things well, so we have to choose wisely based on what we need.
Memory Capacity: AI needs a lot of memory to work with big datasets. Enough memory allows us to process more data simultaneously, making our computations faster.
Bandwidth: AI needs to move lots of data around quickly, especially when learning and doing tasks.
Energy Efficiency: Since AI uses a lot of computing power, we want to use hardware that doesn't waste too much electricity.
Scalability: As AI gets used more, we need to make sure our computers can handle bigger workloads.

In Web2, computation resources are monopolized by centralized companies that invest exorbitant amounts of money into them to hasten AI training and develop more intricate AI algorithms and models. Furthermore, to maintain their competitive edge, they must consistently invest in hardware and overhaul the underlying algorithms to address scaling issues.

To make matters worse, these resources are often underutilized, resulting in inefficient resource allocation and wastage. And let's not forget the colossal energy consumption required to keep the entire system operational.

Centralized computing resources pose a major obstacle for AI startups and researchers because of the exorbitant hardware costs and the significant expenses linked with resources and energy. Fortunately, Web3 has the potential to revolutionize this landscape by providing a more equitable and efficient AI ecosystem through the decentralization of computational resources. With this shift, we can expect an outpouring of innovation and a vast array of AI applications.

The distributed computation blockchain technology has immense potential for AI tasks, particularly for training expansive language models or sophisticated deep learning algorithms. Harnessing a network of distributed resources substantially enhances the processing power available for AI development. This approach proposes an economic framework that rewards participants for contributing computational resources, fostering a self-sustaining ecosystem where AI developers gain access to necessary resources and providers are fairly remunerated in cryptocurrencies or other digital tokens.

This model approach allows AI developers to utilize computational resources as needed without the commitment to substantial infrastructure investments. This model is highly cost-effective compared to traditional cloud services, especially for projects with variable workloads or unpredictable demands.

In summary, the distributed computation blockchain offers a powerful, efficient, and economically viable platform for AI development. Its undeniable benefits make advanced computing resources more accessible to a broader range of innovators.

Web3 for AI Algorithms

Data is the fuel that powers AI, but algorithms are the powerhouse that drives its capabilities. Without algorithms, data remains a collection of meaningless 0s and 1s. Algorithms translate raw data into meaningful insights and intelligent actions, forming the core of "artificial intelligence."

Looking back at the history of AI, we see periodic breakthroughs in algorithm development, each driving a new wave of innovation and advancement. From symbolic AI to machine learning, support vector machines, and deep learning, each generation of algorithms has expanded AI's capabilities and opened up new possibilities.

The complexity and sophistication of AI algorithms have increased significantly over time, making it increasingly challenging for individual researchers or organizations to make significant breakthroughs. This is partly due to the computational demands of modern AI algorithms, which often require specialized hardware and extensive training data.

Fortunately, Web3 technologies offer a suitable framework for facilitating collaborative AI initiatives, emphasizing distributed computing, decentralized governance, and open collaboration. Web3 platforms provide a secure and transparent environment for sharing data, models, and computational resources, enabling researchers and developers to collaborate effectively on AI projects.

Web3s Incentivization of AI

It is essential to dedicate a separate section to a comprehensive demonstration of the monetization of AI with Web3. We have previously discussed this topic, but it is so crucial that it deserves a more assertive approach. The shift in the profit paradigm that Web3 monetization for AI represents is a game changer, moving from resource controllers' earnings to contributors' earnings.

In Web2, money is usually made like this:

People who gather and move around data make money because they control it.
Those who train artificial intelligence (AI) also make money because they control the data and the methods used to teach the AI.
Companies implementing AI make money by controlling the resources needed to make AI work well and meet market needs.

Now, here are some questions:

Do people who create data make money from it?
Do the people who develop new AI methods make money from their research?
Is everyone involved in improving AI's speed and efficiency, tweaking algorithms, and making small improvements to earn the rewards it deserves?

In reality, the answer is likely to be negative. Although there can be some payment for data collection, patent transfers, licensing, and rewards and salaries for engineers and researchers, it is difficult to ascertain whether contributors receive fair compensation.

Beside the profit made, Other important questions to consider include:

How does the company collect data to make sure the data is accurate?
How can the person who produces and owns the data be sure it's not being used inappropriately without their permission?
How does the AI company prepare the data it receives from suppliers, and how should it be?
How can the person or company using resources in this process be sure they're not paying too much for what they're getting?

The answers to these questions are insufficient within the confines of the Web2 paradigm. To overcome these challenges, it is time to embrace the advancements of Web3 technologies, which greatly emphasize decentralization, tokenization, transparency, and open collaboration. Web3 empowers individuals and organizations to take charge of their data and provides mechanisms for sharing AI models and computational resources, fostering a more inclusive and equitable AI ecosystem.

In Web3:

People who create data get paid for what they contribute.
Those who gather and move data around get paid for doing so.
People who invent algorithms get paid for their valuable creations.
Companies specializing in AI get paid for providing computational power, training, and important data.
Production companies make money by selling what they make, and they can use blockchain technology to reduce misuse of their products.

Arguably, there can be new kinds of markets, such as:

High-quality data is more valuable in data markets because it's treated like an asset.
In data processor markets, different companies specialize in processing different data types. They make money by providing ready-to-use data to customers, like AI trainers, who need it to improve their models.
In AI model markets, there's a place where people can buy and sell different AI models. Users can pick the one that suits their needs best, creating a marketplace that focuses on finding the right AI model for each job.
Application markets are similar to regular app stores, where people buy the apps they want to use. However, in Web3, a feature called on-chain access control helps prevent misuse and makes the environment more secure for app providers and users.

The utilization of Web3 and its incentivizing mechanism ensures that individual players in the AI cycle are rewarded fairly for their efforts or face appropriate consequences for any malicious behavior.

Conclusion

The intersection of AI and Web3 holds tremendous promise for transforming various sectors and industries. By leveraging both technologies' strengths, we can create more effective, transparent, and ethical AI systems that benefit society.

From data management to computation and algorithm development, Web3 technologies like blockchain and decentralized networks can potentially resolve some of the most pressing concerns facing the AI sector. These technologies enable greater collaboration, transparency, and incentivization, ultimately leading to better AI models, improved data quality, and more equitable distribution of resources.

While there are still challenges to overcome, the future looks bright for AI and Web3. As these technologies continue to evolve and mature, we can anticipate groundbreaking innovations and applications that will reshape the way we live, work, and interact with one another. Ultimately, the fusion of AI and Web3 has the potential to bring about a new era of growth, collaboration, and positive transformation. It's up to us to harness this potential and create a brighter future for all

Top 15 Distributed Computing (DePIN) Tokens by Market Capitalization (2024)

SpheronStaff — Fri, 05 Apr 2024 05:25:36 +0000

There are thousands of different cryptocurrencies, such as Bitcoin, Ethereum, Dogecoin, and Tether, which can be overwhelming when you are new to crypto. The total market cap is $ 40.1 billion, with a 24-hour trading volume of $ 1.81 billion. To help you navigate, here is a list of the top 15 Distributed Computing Tokens by Market Capitalization , which indicates the total value of all the currently circulating coins.

Top 15 Distributed Computing Tokens by Market Capitalization

1. Internet Computer (ICP)

The Internet Computer blockchain incorporates a radical rethink of blockchain design, powered by innovations in cryptography. It provides the first World Computer blockchain that can be used to build almost any Web 2.0 online system or service, and web3 services, including web3 social media services, without any need for centralized traditional IT such as cloud computing services.

It also enables smart contracts it hosts to directly create transactions on other blockchains, which in turn enables the full end-to-end decentralization of online services and web3 for the first time. The current CoinMarketCap ranking is #18, with a live market cap of $8,273,723,453 USD.

2. Filecoin (FIL)

Filecoin is a decentralized storage network based on the Interplanetary File Storage (IPFS) protocol. It is designed to utilized unused storage globally into an efficient storage market for users to pay for low cost storage. The objective is to ensure file storage is permanent and distributed across the web. Contrast this with centralized cloud storage solution such as Amazon Web Services, Google Cloud, or Dropbox, where data are stored in servers owned by these private corporations.

Filecoin (FIL or ) is also the currency of the decentralized storage network. Customers wanting to access storage will have to pay for storage in Filecoin. Nodes that provide storage to the network will in turn be paid in Filecoin for their service.

The current CoinMarketCap ranking is #26, with a live market cap of $4,619,111,629 USD.

3. Render Network (RNDR)

Render Network Foundation (The Render Network) is the leading provider of decentralized GPU based rendering solutions, revolutionizing the digital creation process. The network connects node operators looking to monetize their idle GPU compute power with artists looking to scale intensive 3D rendering work and applications to the cloud.

Through a decentralized peer-to-peer network, the Render Network achieves unprecedented levels of scale, speed, and economic efficiency. On top of a decentralized GPU computing network, Render provides a platform for artists and developers to build services and applications for the emerging digital economy, including next generation digital rights management (DRM), artificial intelligence (AI), and virtual assets (NFTs). The current CoinMarketCap ranking is #36, with a live market cap of $3,584,858,552 USD.

4. Bittensor (TAO)

Bittensor is an open-source protocol that utilizes blockchain technology to create a decentralized machine learning network. This network enables machine learning models to train collaboratively and be rewarded in TAO according to the informational value they offer the collective. Additionally, Bittensor's TAO grants external access to users, allowing them to extract information from the network while tuning its activities to meet their needs. The ultimate vision of Bittensor is to create a market for artificial intelligence, allowing producers and consumers of this commodity to interact in a trustless, open, and transparent context.

Bittensor provides a novel strategy for developing and distributing artificial intelligence technology, promoting open access/ownership, decentralized governance, and global access to computing power and innovation within an incentivized framework. The Bittensor network operates using two types of nodes, servers and validators, with assessments based on the value of their responses. Nodes that add value to the network are rewarded with more stake (TAO), while low-value nodes are weakened and eventually de-registered. The current CoinMarketCap ranking is #35, with a live market cap of $3,643,530,790 USD.

5. Theta Network (THETA)

Theta network is a decentralized video streaming network that is powered by blockchain technology. Theta allows users to watch video content and get rewarded with tokens as they share their internet bandwidth and computing resources on a peer-to-peer (P2P) basis. Besides a line-up of institutional investors like Node Capital and DHVC, Theta Network is advised by Steve Chen, co-founder of YouTube, and Justin Kan, co-founder of Twitch.

The current CoinMarketCap ranking is #47, with a live market cap of $2,581,726,693 USD.

6. Arweave (AR)

Arweave is a decentralized storage network that seeks to offer a platform for the indefinite storage of data. Describing itself as "a collectively owned hard drive that never forgets," the network primarily hosts "the permaweb" a permanent, decentralized web with a number of community-driven applications and platforms.

To learn more about this project, check out our deep dive of Arweave. The Arweave network uses a native cryptocurrency, AR, to pay "miners" to indefinitely store the network's information. The current CoinMarketCap ranking is #54, with a live market cap of $2,224,663,184 USD.

7. MultiverseX (EGLD)

MultiversX (formerly Elrond) is a high-throughput blockchain that aims to power the metaverse frontier. MultiversX set out to create a blockchain that is capable of 1000x throughput than most existing blockchains. This improvement of transaction throughput allows MultiversX to handle even the most aggressive wave of user adoption.

eGold (EGLD) is the native token that powers MultiversX. Its utility comprises all core network functionalities, such as staking, governance, transactions, smart contracts, and validator rewards. The current CoinMarketCap ranking is #69, with a live market cap of $1,412,263,326 USD.

8. BitTorrent (BTT)

BitTorrent is a popular peer-to-peer (P2P) file sharing and torrent platform which has become increasingly decentralized in recent years.

Originally released in July 2001, BitTorrent was purchased by blockchain platform TRON in July 2018. Since its acquisition, BitTorrent has added various new tools, with a dedicated native cryptocurrency token, BTT, released in February 2019. BTT was launched on TRONs own blockchain, using its TRC-10 standard.

According to its official literature, BitTorrent is currently the largest decentralized P2P communications protocol in the world. The current CoinMarketCap ranking is #76, with a live market cap of $1,341,084,549 USD.

9. Akash Network (AKT)

Akash Network is a Supercloud spearheading a paradigm shift in cloud computing, disrupting conventional cloud services, and pioneering a revolution in access to essential cloud resources. Leveraging the power of blockchain technology, Akash Network has developed an open-source, decentralized, marketplace for cloud computing, offering an unprecedented level of speed, efficiency, and affordability. This innovation is set to transform the way users perceive and utilize cloud services. The current CoinMarketCap ranking is #93, with a live market cap of $1,021,282,177 USD.

10. Helium (HNT)

Helium (HNT) is a decentralized blockchain-powered network for Internet of Things (IoT) devices. Launched in July 2019, the Helium mainnet allows low-powered wireless devices to communicate with each other and send data across its network of nodes.

Nodes come in the form of so-called Hotspots, which are a combination of a wireless gateway and a blockchain mining device. Users who operate nodes thus mine and earn rewards in Heliums native cryptocurrency token, HNT.

Heliums goal is to prepare IoT communication for the future, identifying inadequacies in current infrastructure from its birth in 2013. The current CoinMarketCap ranking is #106, with a live market cap of $886,766,209 USD.

11. AIOZ Network (AIOZ)

AIOZ Network is a DePIN for Web3 AI, Storage and Streaming. AIOZ empowers a faster, secure and decentralized future. Powered by a global network of DePINs, AIOZ rewards you for sharing your computational resources for storing, transcoding, and streaming digital media content and powering decentralized AI computation. The current CoinMarketCap ranking is #102, with a live market cap of $906,906,955 USD.

12. Ocean Protocol (OCEAN)

Ocean Protocol is an ecosystem for sharing data and associated services. It provides a tokenized service layer that exposes data, storage, compute and algorithms for consumption with a set of deterministic proofs on availability and integrity that serve as verifiable service agreements. There is staking on services to signal quality, reputation and ward against Sybil Attacks.

Ocean helps to unlock data, particularly for AI. It is designed for scale and uses blockchain technology that allows data to be shared and sold in a safe, secure and transparent manner. The current CoinMarketCap ranking is #128, with a live market cap of $652,478,150 USD.

13. IoTeX (IOTX)

Starting as an open-source project in 2017, IoTeX has built a decentralized platform whose aim is to empower the open economics for machines an open ecosystem where people and machines can interact with guaranteed trust, free will, and under properly designed economic incentives.

On top of the IoTeX blockchain, the team has built the essential blocks of infrastructures to connect with Ethereum, BSC, and Heco blockchains such as ioPay wallet (https://iopay.me/) and ioTube bridge (https://iotube.org/), which serve ten thousands of users. IoTeX helps EVM-based DApps scale without concerning expensive gas fees! The current CoinMarketCap ranking is #126, with a live market cap of $678,530,797 USD.

14. Ankr (ANKR)

Ankr is a decentralized blockchain infrastructure provider that operates an array of nodes globally distributed across over 50 Proof-of-Stake networks. This infrastructure helps drive the growth of the crypto economy while powering a full suite of multi-chain tools for Web3 users: The current CoinMarketCap ranking is #150, with a live market cap of $541,507,933 USD.

15. Theta Fuel (TFUEL)

Theta is a Layer 1 blockchain and decentralized infrastructure for Video, AI & Entertainment use cases.

There are two native cryptocurrencies on Theta blockchain: THETA, the staking and governance token, and TFUEL, used as gas for all transactions and on-chain smart contract interactions. The next-generation Edge Network, Theta EdgeCloud, is the first hybrid cloud computing platform built on a fully distributed architecture, set to launch later in 2024. The current CoinMarketCap ranking is #149, with a live market cap of $544,756,580 USD.

Learn how DePIN is ushering in a cleaner future

SpheronStaff — Thu, 04 Apr 2024 04:48:54 +0000

Crypto has had a bad reputation for a while, mostly due to the massive energy consumption of Bitcoin's Proof of Work network. Besides, last year's NFT craze didn't help either, as Ethereum miners worked tirelessly to provide digital images to NFT enthusiasts. However, things are changing for the better.

Ethereum's merger has drastically reduced energy consumption by over 99%, and Bitcoin miners are finding innovative ways to make mining net positive for global energy infrastructures. Despite negative headlines, crypto gradually evolves from a problem to a solution.

The DePIN sector plays a significant role in this development, and many projects contribute to this cause. We've compiled a list of amazing projects that bring back utility to nature and, most importantly, decentralized governance to ESG.

Here are a few sustainable DePIN projects focused on environmental issues.

1. PowerPod - Making strides in the EV industry

Get ready for the launch of PowerPod's interconnected EV charging station network! This project is the perfect way to take action towards a more sustainable future for the automotive sector. Whether you're a homeowner or a business owner, installing a PowerPod charger means rewards for you and for other drivers who use your hardware.

Starting with a smart detachable charging adapter, PowerPod is expanding its line of hardware to include high-performance home AC chargers and an on-the-go charger. All devices will be linked to the blockchain. The project is supported by a dual token model with Energy Points ($PT) and PowerPod Token ($PPD).

2. Arkreen - Bringing green data on-chain

The revolutionary Web3 project Arkreen aims to significantly reduce carbon emissions by collecting green energy data. Its core applications, Renewable Energy Certificates (REC) and Virtual Power Plants (VPP), are set to impact sustainable energy tremendously.

Arkreen's network is the perfect solution for bridging the gap between green energy's demand and supply sides. Its immutable and verifiable data storage layer ensures transparency and trustworthiness in the system.

On the supply side, the network collects information from various equipment and devices, such as solar panels, wind turbines, batteries, and thermostats, which provide valuable data. On the demand side, Arkreen has a wide range of applications, and users can access various types of miners, including remote miners, standard miners, and even virtual API miners.

3. Aquasave - water management innovation.

Aquasave is a project that aims to track and analyze the Earth's water supplies. It provides insight into the quality and consumption of our planet's most vital resource. The project uses user-generated data to provide statistics and reports that enable more efficient decision-making at local and global levels. Incentivizing users to generate data, Aquasave brings awareness to responsible water use and provides actionable insights to key decision-makers. By doing so. It is a great example of the flywheel effect DePINs are known for.

The project monetizes the data generated by its users using its native $AQC token. Participants can earn $AQC tokens by contributing to the network. Aquasave is a project to watch, as there are still plenty of roadmap milestones to be met in 2024. These include the device presale, token launch, and stock exchange listing.

4. WeatherXM - The era of Weather 3.0

Have you ever noticed that your friend's weather app gives a different forecast than yours, even though you're standing right next to each other? Forget about that. With WeatherXM, you can access the most accurate weather data available in the market, thanks to its network of people-owned weather stations.

The WeatherXM network offers three weather stations with different connectivity options and price points. Choose the Wi-Fi station for $400, the Helium-LoRAWAN station for $400, or the 4G-LTE station for $900, and start enjoying the best weather data out there.

Contributing to the WeatherXM network can earn you $WXM tokens. Whether it's sunny or rainy, you can always be part of the network and earn rewards. To get started, visit the WeatherXM website today. It's essential to note that WeatherXM is a top-level DePIN project that has received funding from various sources, including a $1.5 million investment from Borderless Capital.

5. Powerledger - Decentralized energy since 2016

Powerledger is a trailblazer in the DePIN industry, having been one of the space pioneers long before it was even called DePIN. Over the years, the company has won several accolades for its outstanding achievements.

Since its establishment in 2016, Powerledger has been working towards creating an efficient marketplace that can effectively handle the fluctuations in renewable energy compared to more stable fossil fuel sources. The company has achieved this by developing solutions for tracking, tracing, and trading renewable energy.

Powerledger has used the Ethereum blockchain to secure its $POWR token and leveraging Solana's technology to build its custom Powerledger Energy Blockchain.

The energy market is transforming from centralized to decentralized, and DePIN projects like Powerledger are at the forefront. These innovative projects tackle real-world challenges in agile ways that governments and corporations cannot compete with. People worldwide align with these projects' intentions to do good and give back while being paid for their contributions, and it's only right.

Conclusion

However, it's worth noting that these are DePIN verticals with some of the most stringent regulatory measures, particularly in the energy sector. Messari's recent report highlights that revenue in energy, compared to other verticals, has yet to take off.

This could be viewed as a warning sign but also indicates the potential for significant growth. So, let's buckle up for an exciting ride ahead!

From Messari and EV3's State of DePIN 2023 report

With hundreds of DePINs gaining momentum, launching their products, and expanding their networks, the year ahead will be wild. Weve got you covered!

6 DePIN Devices Bringing Crypto to Reality (2024)

SpheronStaff — Wed, 03 Apr 2024 04:50:16 +0000

We won't be able to onboard millions of users into cryptocurrency if we have nothing to offer them. It's easy to discuss the advantages of decentralization, but convincing people to change their habits is a different story. DePIN provides a physical connection to the blockchain, introducing people to the exceptional problem-solving capabilities that cryptocurrency networks can provide. This is made possible by some amazing products that have recently hit the market.

Let's examine some of the signature devices leading the current wave of cryptocurrency adoption.

1. NATIX on your smartphone

Do you know that your smartphone could be the gateway to DePIN? With its advanced sensors such as microphone, camera, gyroscope, temperature, accelerometer, and more, your smartphone can achieve a lot. NATIX is a project that uses a smartphone app to tackle the issue of fresh map data. The app is free to download and use, making it accessible to everyone. You can download the NATIX app now and use "hotspot" as your referral code to get 35,000 bonus points.

2. Wicrypt Spider Wi-Fi Hotspot

Wicrypt is addressing the issue of internet connectivity in Africa by providing a range of long-range WiFi routers. One of their products, the Spider hotspot, can support up to 70 simultaneous connections and cover distances of up to 200m. The hotspot provides users with WiFi internet (up to 300 Mbps) using a SIM card slot and 4G-LTE receiving capabilities.

This is an excellent solution for highly populated areas like markets, cultural centers, and sporting venues, as well as residential areas. Business owners can benefit from investing in this product since it can provide WiFi access to their customers, while also earning them $WNT tokens for contributing to the growth of the Wicrypt network.

This product is perfect for the African market and currently retails for around $270. It can be purchased using USDT and USDC or through their Shopify store for more traditional payment methods.

By providing reliable, expandable, and affordable internet solutions, Wicrypt is providing a foundation for a thriving African DePIN ecosystem

3. ATOR Relay

ATOR is developing an anonymity layer to route traffic for DePIN projects. The ATOR Relay will function as the entry point to this layer. This is a lightweight model that doesn't require a 19-inch rack for housing. Security is of the utmost importance, and the model comes equipped with an encryption chip and CryptoAuth key generation. It can support 1,000 Mbps Ethernet and also works on WiFi, and it can be easily configured via USB.

Users who run nodes on the ATOR network will be eligible for $ATOR tokens. However, if you use ATOR's official Relay, you won't need to lock up an additional 100 $ATOR tokens for 180 days, which is an added bonus.

The first 1,000 units were reserved in a presale last year. During this sale, users could mint an Atornaut NFT for 0.1 ETH. Once these NFTs are ready to ship, owners will be invited to pay 250 $ATOR to redeem their Relay. If you want to get in on the action, there are a few Atornaut NFTs trading on OpenSea.

In addition to the official Relay model, ATOR will allow other DePIN hardware to operate through its network. They've partnered with Hotspotty to assist with this integration.

4. DIMO Macaron

The DIMO Macaron is a device that can be easily plugged into your car's OBD-II (onboard diagnostics) port. This port can be found under the dashboard of most modern cars and is used by mechanics to access important data about your vehicle. The Macaron is compatible with cars dating back to 2008, so you don't need to worry about having a brand-new car to use it.

The Macaron sends data over the Helium IoT network and includes three years of connectivity in its affordable $99 price. It's small enough to be unobtrusive for the driver, and it's powered by the car itself, so there's no need to replace batteries.

5. Weather XM WS2000 Weather Station

Weather is an essential part of our daily lives, and Weather XM has devised a unique solution to increase forecast accuracy by adding more weather data collection points. Unlike traditional centralized stations, Weather XM's various stations allow users to combine their personal station data to create hyper-localized weather reporting.

These advanced weather stations are far superior to the old-school mercury meters, and they can monitor a variety of factors, including atmospheric pressure, temperature, precipitation, humidity, wind, and UV. Installing a Weather XM station is easy, and you can even earn $WXM tokens by owning one. The project offers three stations, each with a different connectivity option and price point. The WS2000, which is pictured above, sends its data over LoRaWAN and is preconfigured for the Helium network. This particular station costs $400.

If you're interested in purchasing one of these weather stations, head over to the WeatherXM shop to order your station. Whether it's raining, sunny, or anything in between, you'll never be caught off guard again.

6. GEODNET MobileCM Triple-Band GNSS Base-Station

NET is using a decentralized solution to develop its Real-Time Kinetic (RTK) correction network. Sophisticated antennasimprove positioning accuracy to the centimeter level. The owners of these antennas are also rewarded with GEOD tokens. The DePIN deployers are essentially mining the sky!

The GEODNET triple band GNSS base station is compatible with the four major constellations: GPS, Galileo, GLONASS, and BeiDou, which makes it satellite agnostic. A clear line of sight to the sky is required to get it working, but this can be achieved in both urban and rural environments.

You can buy a GEODNET mining station for $695. Interestingly, there are reward multipliers available (up to 14x) to encourage map coverage. If you're targeting one of these regions, your ROI could be secured quicker than expected. You can read more about it in our recent GEODNET article.

These units are currently in high demand, and deliveries are expected to ship as soon as possible. We recommend contacting EasyNav, which has worldwide distribution rights. Make sure to use the discount code HOTSPOTTY when contacting them. Providing precise positioning will be a high-value service in a world of autonomous devices.

Check out the awesome projects listed below.

Conclusion

If you enjoy managing a group of devices and creating a coverage map for your favorite projects, then working at DePIN might be the perfect fit for you. Rather than working on a protocol level, you can work as a deployer and experience the excitement firsthand.

How to Scale a DePIN Hobby to Monthly Side Hustle

SpheronStaff — Tue, 02 Apr 2024 05:12:33 +0000

If you've been actively searching for DePIN-related content lately, you must have stumbled upon the DePIN Connection YouTube channel by Bradley Meyer. Let us tell you, Brad is not just your average DePIN deployer; he is also a highly successful one. DePIN Hub recently contacted him to gain insights into his journey, what drives him, and his advice for those who aspire to follow in his footsteps. The $50K newsletter title is undoubtedly the real deal, as we'll soon discover below.

From Quarantine to Success

In 2020, Brad found himself in a 14-day quarantine and took advantage of the extra time to delve into the blockchain world. After discovering Helium, he set up shop in the DePIN space and became a successful entrepreneur. A long-term Hotspotty customer, Brad used their services to manage, optimize, and scale his Helium hotspot operations. Thanks to Hotspotty, Brad now runs 4 DePIN business lines, 3 built around deployments with impressive installation and fleet stats. The 4th line is focused on his DePIN YouTube channel, which has also contributed to his success.

Helium (60 rooftop installs) 📡
DIMO (170 vehicles connected) 🚗
Hivemapper (55 full-time drivers) 🗺

DePIN Earning Deployments

Brad's hobby has grown into a highly profitable business, earning him around $40-50k monthly across his DePIN projects. However, it is crucial for any deployer to thoroughly research a project's tokenomics and understand the implications of reward distributions.

Predicting earnings can be tricky, especially when working at scale for popular projects with extended delivery backorders.

To achieve growth in DePIN deployment, fleet management's hosting model is a crucial component. Brad initially scaled his Helium operations through Hotspotty's best-in-class fleet management software. Referrals have played a significant role in his growth, with approximately 40% of his hosts referred by other hosts.

Evaluating DePIN Projects

When it comes to choosing a DePIN project to invest your time and money into, the abundance of options can make it difficult to find the right one. However, Brad's 4-step checklist can help you evaluate potential projects with confidence.

Utility: What is the use case? How much revenue can the project generate? What is the differentiator that will capture market share?
Participation: Who is the target audience? What are the barriers to entry? Can the project gain enough participation to generate the necessary infrastructure or data?
Team: Are they doxxed? Do they have proof they can execute?
Tokenomics: How are the tokens distributed between the participants, the team, investors, and other categories? Are there any red flags?

It's not just Brad who has benefited from the DePIN projects. Many others, such as 5 Mile Trucker, have also seen significant success. 5 Mile Trucker has driven an impressive 1.8 million safe miles and earns money through the Natix network. This highlights the potential for profitability in the DePIN space and the variety of opportunities available to those involved.

Diego, a content creator on YouTube, generated $3,200 in passive income over a month through his involvement in the DePIN sector. He shares his experiences and strategies in a dedicated video on his channel, providing valuable insights for those interested in replicating his success.

In February, the CryptoJar YouTube channel received $759.33 in passive income through its involvement in the DePIN project. If you're interested in learning how they achieved this, check out their video detailing their experience.

Best DePINs to keep an eye out for

Conclusion

In conclusion, a career in professional DePIN deployments can be a promising and rewarding path for individuals passionate about blockchain technology and willing to invest time and effort in research and development. There is significant potential for growth and success in this industry, particularly for those who approach it with a clear understanding of the risks involved and a well-thought-out strategy. To succeed, it's essential to carefully select the right projects, start small, and gradually scale up operations while continuously refining one's processes. With dedication, patience, and a long-term perspective, professionals in this field can achieve personal financial success and contribute to the evolution and adoption of innovative technologies that can potentially transform industries and society.