“Building a Sustainable and Developer-Friendly GPU Cloud” — that is the founding mission behind Medjed.ai. If you’re a researcher, engineer, or lab building AI models, read on: here’s how we’re doing things differently.
The Landscape: What’s Missing
Many current GPU cloud providers target large enterprises or are optimized around containerized workloads. But as any AI dev knows: sometimes you need full system access — custom drivers, unusual configurations, non-standard dependencies. That’s where containers or shared environments break down.
Also, with so many retired / end-of-life GPU servers every year, there is a huge waste of potentially useful compute. Yet often you end up paying high premiums or dealing with opaque infrastructures.
What Medjed.ai Brings to the Table
Green / ESG-Friendly Approach via Reuse
Medjed.ai repurposes retired GPU servers — hardware that still has plenty of life for many AI training & inference tasks. Instead of letting these machines sit idle (or be scrapped), we refurbish & re-deploy them. That yields major environmental benefits: reducing electronic waste, spreading embodied carbon over a longer hardware lifetime. At the same time, it allows us to offer lower cost to users, because the capex burden is lower. Medjed AI
Cloud KVM GPUs: True Isolation & Full Control
Our Cloud KVM GPUs give you virtual machines with Kernel Virtual Machine (KVM) isolation. Why this matters:
You get root access / full OS control: install what you want, tweak drivers, use specialized stacks.
Better security and isolation than “shared container” models. Your data, code, and dependencies are more separated from others.
More predictable performance: since your VM is allocated its share of GPU & system resources (no noisy neighbor in a container).
Developer & Laboratory Friendly
Medjed.ai was built for folks like you — labs, individual developers, research groups — not just big corporations. Key priorities:
Transparency and predictability in pricing and service.
Avoiding vendor lock-in: since you are working with standard VM/KVM environments, moving workloads in or out is easier.
Community, documentation, observability: we want you to see what’s going on, to debug, to experiment.
Better Data Security via Isolation
Cloud KVM provides a strong isolation boundary. Your data stays in your VM, your disks; there’s less shared kernel or host infrastructure. Combined with standard best practices for cryptography, network isolation, virtual machine snapshots, and backups, we aim to provide stronger guarantees of data safety.
Comparing Medjed.ai to Alternatives
Why Sustainability & Cost Go Hand-in-Hand
By extending hardware lifetimes, Medjed.ai spreads the fixed environmental & capital cost of GPU hardware over more usage. That means:
Lower cost for users without compromising capacity.
Less e-waste, less frequent manufacturing supply chain impacts.
Alignment with ESG (Environmental, Social, Governance) goals–something many labs, universities, and organizations increasingly care about and must report on.
For Labs, Researchers, Developers: What to Expect Soon
We are rolling out Cloud KVM GPUs (in addition to existing bare metal and colocation options). These will give you:
Better isolation for experiments.
The ability to run sensitive workloads, regulatory / compliance requirements.
Cleaner boundaries between projects/teams.
Plus, we expect regular improvements: more GPU types (for different performance/budget trade-offs), better tooling around VM snapshots, security audits, monitoring, etc.
Final Word
If you’ve ever thought:
“I’m paying too much for less control,” or “I wish I could use older GPUs more cheaply but safely,” or “I want my AI environment close to the metal,” then Medjed.ai might be the GPU cloud you’ve been waiting for. It’s green, it’s secure, and it’s built with devs and labs in mind.
If you’re interested, check out our docs or get in touch with the community—feedback, early access, suggestions all welcome. Let’s build more sustainable, more independent AI infrastructure together.
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