Understanding LLMs & Ideating A Decentralized Approach To Solve Challenges

Artificial Intelligence (AI) is not just trending anymore; it has transformed into a global phenomenon. Every developer wants to work with its innovation, every application wants to integrate its features, every user wants to interact with it and experience the advantages.

Those who help build the ascendancy of AI know that it is the Large Language Learning Models (LLMs) like GPT4, BERT, T5, etc, that power up AI models. This has also led to the rise and development of fields like machine learning (ML) and natural language processing (NLP). If we look into the fundamentals of the NLP process, it essentially involves the understanding and generation of human languages by training massive datasets models, based on deep learning architectures, especially transformer architecture.

The 2017 paper “Attention is All You Need” (Vaswani et al.) underlines the 5 key components that transformers use to process input data.

1. Embedding layer

2. Encoder & decoder

3. Self-attention mechanism

4. Feed-forward neural networks

5. Layer normalization and residual connections

This leads to the next phase, where LLMs actually get trained. First, data is collected from diverse sources like books, articles, websites, etc. Then the text data is cleaned, formatted, and tokenized into manageable units. When it comes to objective functions, it can be either Causal Language Modeling (CLM) as used in GPT models to predict the next word in a sequence of words, or Masked Language Modeling (MLM) as used in BERT models where some words in a sequence are masked and the model predicts the words based on context interpretation.

After the model parameters are optimized as part of the unsupervised training process (pre-training phase), LLMs undergo supervised training and transfer learning (fine-tuning phase). This involves task-specific datasets as well as adapting the models. The whole process culminates in inference and generation based on analysis of learned patterns and knowledge.

The discussion so far did not touch upon the topic of challenges and considerations, which are mainly three-pronged.

• Computational resources

• Bias and ethics

• Interpretability

This is the part where the idea of a future with decentralized AI (DeAI) starts making sense. The reason Oasis becomes integral to the discussion is that it has been building primitives for responsible AI in line with its privacy-first vision long before applied AI had permeated so many aspects of our lives. The decentralized confidential computation (DeCC) capabilities of Oasis make the DeAI approach simple and seamless with configurable and verifiable privacy.

So, if you are a developer working with LLMs and maybe thinking of deploying applications based on the trained models, consider moving away from the centralized paradigm and embracing DeAI, where you only trust after verifying. You will also benefit from the latest innovation that Oasis has been perfecting - the ROFL (Runtime Off-chain Logic) framework that can leverage NVIDIA TEEs, making it possible for AI models to stay private while maintaining verifiability. One of the direct results of the algorithms developed by Oasis Labs that run in ROFL is it makes the evaluation of fairness in AI models possible, ensuring they are unbiased.

Let's discuss in the comments what you think of the challenges traditional, centralized LLMs face, and the idea of potential solutions in a transformative and synergistic collaboration with blockchain technology.

Comments

[Dotallio]

100% agree, privacy and trust worries really hold back a lot of enterprise LLM use for me.
Do you think decentralized frameworks like ROFL can actually gain wide adoption, or will most teams stick with the 'safe bet' of big centralized providers?

[DC]

I believe in decentralized AI being able to provide many of the answers to challenges that centralized AI and enterprise LLMs are not even focusing on, let alone solving them. Why the ROFL framework works, imo, is that as AI evolves, the datasets will grow exponentially, and their handling and processing will only get more overwhelming. So, while on-chain confidentiality is what we need, with the help of ROFL, off-chain verifiability can be factored in too, making the overall performance better and more efficient.

[Umang Suthar]

This was a great breakdown of LLMs and the growing relevance of decentralized AI. The challenges around infrastructure cost, model privacy, and opaque inference pipelines in centralized setups are very real, especially for developers looking to scale AI applications independently.

We’ve been exploring similar directions, especially around how on-chain infrastructure can reduce LLM hosting cost and completely remove backend complexity for devs. Decentralized compute, verifiable execution, and transparent AI logic could truly reshape how future AI applications are built and run.

Curious to hear from others—what would it take for devs to actually switch to a decentralized AI stack in production?