vLLM

vLLM is an open-source library for fast LLM inference and serving, originally developed at UC Berkeley's Sky Computing Lab. Where lightweight runtimes target a single user on one machine, vLLM is built for throughput: serving many simultaneous requests efficiently, which makes it the tool of choice when self-hosting a model for a team, an application, or a small Hashcenter that combines mining and compute. If Ollama is the personal workstation of local AI, vLLM is the production floor.

Key techniques

vLLM's signature innovation is PagedAttention, which manages the attention key-value (KV) cache in non-contiguous memory blocks, much like virtual-memory paging in an operating system. Naive serving reserves one big contiguous slab of VRAM per request sized for the worst case; PagedAttention allocates small pages on demand, cutting the waste dramatically and letting far more concurrent requests share the same GPU. It pairs this with continuous batching, which adds and removes requests from the running batch dynamically rather than waiting for fixed groups — a finished request's slot is refilled immediately, keeping the GPU saturated. Prefix caching reuses computation for shared prompt beginnings (a big win when every request carries the same system prompt), while chunked prefill and speculative decoding smooth latency further. The same levers that matter on any rig apply here too: quantized weights stretch what a given card can hold, and the KV cache grows with the context window, so long-context serving is a memory-budget exercise above all.

Hardware and compatibility

vLLM supports NVIDIA and AMD GPUs, x86/ARM/PowerPC CPUs, and specialized accelerators, and runs 200+ model architectures pulled straight from the Hugging Face Hub. Crucially, it exposes an OpenAI-compatible API server, so existing client code — apps, agents, RAG pipelines — can target a private vLLM deployment by changing a base URL. That compatibility is the recurring theme of sovereign AI tooling: keep the familiar interface, remove the dependence on someone else's servers.

Operationally, vLLM deploys like any modern service: a Python package or container that loads a model and exposes an HTTP endpoint, with metrics for throughput, latency, and cache hit rates. Tensor parallelism spreads a model too large for one card across several GPUs in the same box, which is how home-lab operators serve models that would otherwise require datacenter hardware — two or four consumer cards pooling their VRAM into one logical accelerator.

When to reach for it

The honest rule of thumb: one user, one machine, interactive chat — a single-user runtime like llama.cpp or Ollama is simpler and entirely sufficient. Multiple simultaneous users, an application backend, batch processing jobs, or any workload where GPU utilization translates to money — vLLM's batching and memory management pay for their added operational complexity many times over. For the sovereignty-minded operator the appeal is structural: a vLLM box behind your own firewall is a drop-in replacement for a cloud AI API, serving your family, your business, or your customers with no per-token bill, no usage log leaving the building, and no terms-of-service between you and your own compute. Like a well-tuned hashboard, a well-run vLLM server is about extracting every unit of useful work from silicon you own — and the project's open-source license means the serving layer itself is infrastructure you can audit, fork, and keep.

The project moves quickly, with an active open community and frequent support for new model families shortly after release — worth knowing when planning upgrades, since serving infrastructure you control also means an update cadence you control.

For single-user local runtimes, compare llama.cpp and Ollama above; for where the models come from, see the Hugging Face Hub. Together they cover the sovereign serving stack from bench experiment to production.

Find serving stacks in the sovereign self-hosting catalog.