Selling whole dedicated servers with multiple Nvidia A100 or H100 cards is fine for enterprise customers, but the vast majority of web developers, startups, and small business clients only need a fraction of that power to run their private Llama instances.

To maximize the profitability of your datacenter racks, you need to implement efficient GPU virtualization (vGPU) or container-level resource sharing. This allows you to split a single physical graphics card among multiple separate VPS nodes or virtual machines.

But let's look at this from a strict systems engineering perspective - setting up proper multi-tenant isolation for GPU workloads isn't a task for beginners. If you mess up your security boundaries or fail to enforce rigid resource caps, one client running a massive unoptimized batch inference job on a Llama-3-70B model can easily freeze the entire physical card, bringing down all adjacent virtual environments. Highly unlikely to end well.

I want to use this thread to break down our shared technical blueprints for virtualizing open-source LLM environments:

What software stack are you running to segment your hardware? Are you using enterprise Nvidia vGPU drivers on Proxmox/ESXi, or deploying lightweight isolated Docker containers via nvidia-container-toolkit?
How do you strictly limit the Maximum VRAM usage for each tenant to prevent out-of-memory (OOM) kernel kills on the host node?
Are you wrapping your Llama deployments inside automated Kubernetes clusters or utilizing simpler orchestration pools like vLLM / Ollama backends for high-density client distribution?

If you've successfully engineered a multi-tenant GPU network for Llama hosting, share your architecture.