Run Your Own AI Stack on GPUs You Control

Token bills add up. Data leaves your network. Rate limits hit at the worst time. At some point every team building on LLMs asks the same question: what would it take to run this ourselves?

Usually the answer is “a platform team and a month of yak shaving.” Drivers, CUDA versions, serving frameworks, gateways, observability, and none of it is your actual product.

On RunOS, the whole stack is one-click services on hardware you control.

Serving: vLLM and Ollama

vLLM is the serious serving path: high-throughput inference for open models with an OpenAI-compatible API, so existing clients and SDKs point at it with a base URL change. Run two or more replicas and RunOS puts a router in front that’s KV-cache aware, sending requests where the cache is warm. LMCache support adds hot and cold cache tiers, backed by Valkey and MinIO, for long prompts you see repeatedly.

Ollama is the lighter path: pick models from a catalog, install them, and chat. Great for smaller models, internal tools, and getting a feel for what your hardware can do.

The gateway: LiteLLM

LiteLLM gives your team one endpoint and one set of keys in front of everything: your self-hosted models and external providers alike. Virtual keys per team or app, budgets, rate limits, and spend logs. When you want to route some traffic to your own GPUs and some to a commercial API, this is where that policy lives.

Seeing what’s happening: Langfuse

Langfuse runs as a managed service too: tracing for every call, prompt versioning, and evals. Self-hosted inference without observability is how you end up guessing; this closes that gap on your own infrastructure.

And because managed PostgreSQL on RunOS supports the pgvector extension, your embeddings can live next to everything else.

The GPU part, handled

GPU nodes are where self-hosting usually gets ugly. RunOS manages the NVIDIA GPU operator for you: driver installation with compatibility guardrails, per-GPU telemetry, and MIG partitioning so one big card can serve several workloads with real isolation.

You can bring your own GPU hardware, or rent GPU machines from clouds like Lambda and Hyperstack and join them as nodes with the same one-command flow as any other server.

The pitch in one line: an OpenAI-compatible endpoint, with budgets and tracing, on GPUs whose monthly cost you know exactly.

The honest bits

vLLM needs CUDA-capable NVIDIA GPUs; Ollama can run CPU-only for small models, but real serving wants the GPUs. You choose replica counts and they stay fixed, so capacity planning is yours to do. And big models take a while to load on cold start, which is normal and worth planning around.

Own your inference.

Start free and stand up vLLM on your first GPU node. The managed services docs cover the full catalog.