What vLLM is and why LLM inference is the bottleneck
vLLM is an open-source inference engine for large language models, originally from UC Berkeley's Sky Computing Lab and now a broadly adopted community project. It addresses the part of an AI system that is easy to underestimate: LLM inference, actually generating tokens efficiently once the model is trained. For anyone serious about self-hosting LLMs or LLM hosting in Germany, inference is where cost, latency and hardware utilisation are decided. Training happens once; inference runs every single time a user sends a request. vLLM exists to make that step fast, predictable and affordable on your own GPUs rather than on a third-party cloud API.
The core problem is memory, not raw compute. During generation a model keeps a KV cache, the attention keys and values for every token already processed. This cache grows with sequence length and with the number of concurrent requests, and it dominates GPU memory. Naive serving wastes much of that memory through fragmentation and over-reservation, which forces small batch sizes and leaves expensive accelerators idle. vLLM's central insight is to manage KV-cache memory the way an operating system manages RAM.
PagedAttention: KV cache like virtual memory
PagedAttention is vLLM's defining technique. Instead of allocating one large contiguous block of memory per request, it splits the KV cache into fixed-size blocks, pages, and maps them through a block table, exactly like virtual memory paging in an operating system. The benefits are concrete: near-elimination of internal fragmentation, so memory that would otherwise be wasted is available for more concurrent requests; larger effective batches on the same hardware; and cheap sharing of identical prefixes. When many requests share a common system prompt or a sampling tree branches, the shared pages are stored once and referenced, rather than copied. The practical result is markedly higher GPU memory efficiency, which translates directly into more users served per card.
Continuous batching and the API surface
The second pillar is continuous batching, sometimes called iteration-level scheduling. Traditional static batching waits to assemble a batch, runs all requests to completion together, and only then admits new ones, so a single long generation stalls everything behind it. vLLM instead schedules at the granularity of individual decoding steps: finished sequences leave the batch immediately and waiting requests join on the very next iteration. This keeps the GPU saturated under mixed, real-world traffic where prompt lengths and output lengths vary widely, and it is the main reason vLLM sustains high throughput.
Around the engine, vLLM ships an OpenAI-compatible API server. It exposes the familiar /v1/chat/completions and /v1/completions endpoints, so existing tooling, SDKs and gateways work against a self-hosted model with little more than a changed base URL. That compatibility is what makes a migration away from a commercial API a configuration change rather than a rewrite.
Scaling up: tensor parallelism, quantisation, supported models
A single GPU cannot hold the largest models, so vLLM supports tensor parallelism: the weights of each layer are sharded across multiple GPUs that compute in lockstep, letting a 70B-class model run across several cards in one server. For larger deployments this combines with pipeline parallelism across nodes. Memory pressure is reduced further by quantisation, vLLM supports formats such as AWQ, GPTQ and FP8, which shrink the weights (and, with FP8, the KV cache) so a model fits on fewer or smaller GPUs at a controlled, measurable accuracy trade-off you should validate on your own tasks.
On the model side, vLLM covers the mainstream open ecosystem: Llama, Mistral and Mixtral, Qwen, Gemma, Phi, and German- and Europe-focused efforts such as Teuken (OpenGPT-X/Fraunhofer, open weights under Apache 2.0), among many others. Because these are open weights, you can run them entirely on infrastructure you control, the foundation of genuine sovereign AI.
vLLM vs. Ollama and TGI, and the throughput/latency trade-off
vLLM is not the only option, and the honest answer is that the right tool depends on the goal. Ollama is excellent for local experimentation and single-machine, single-user setups: trivial to install, friendly defaults, modest hardware. Hugging Face TGI (Text Generation Inference) is a mature, Rust-based production server with a similar high-throughput design; note, however, that Hugging Face moved TGI into maintenance mode in late 2025, it is still maintained but no longer actively developed, which makes vLLM the more actively evolving choice for new production deployments today. vLLM tends to shine when you need maximum throughput and concurrency on GPU servers, many simultaneous users behind one deployment. A reasonable rule of thumb: Ollama for a developer laptop or a small internal tool, vLLM for a shared production service (TGI still viable where its maintenance status is acceptable).
Throughput and latency pull against each other, and the balance depends on your GPU model, the model size, the context length and the concurrency level. Large batches maximise tokens-per-second across all users but can lengthen the time-to-first-token for any single request; small batches do the reverse. There is no universal benchmark number that transfers to your setup, anyone who quotes one without naming the hardware, model and load is guessing. Capacity should be sized against your own representative workload.
How Beyonetix runs vLLM in Germany, and what comes next
Beyonetix operates vLLM on company-owned GPU servers in Germany, serving open models across the 7B-to-70B range. In front of the inference engines sits a LiteLLM gateway that presents a single OpenAI-compatible endpoint, routes requests across models and instances, handles keys and budgets, and centralises logging, so applications target one stable interface while the models behind it can change. Crucially, prompts, documents and generated text never leave infrastructure under client control: data residency in Germany and no default dependence on US models. That is a strong technical foundation for organisations bound by GDPR or sector-specific confidentiality. We make no blanket compliance guarantee and hold no ISO or BSI certifications: whether a specific setup is GDPR-compliant depends on the system as a whole (contracts, technical and organisational measures, processing purposes) and is a matter for your data protection officer or legal counsel to assess. The same stack powers our citation-grounded retrieval work, for example the large production knowledge archive, where answers must be traceable to sources. You can read more about our AI infrastructure and hosting.
Looking ahead, the inference layer keeps maturing fast: speculative decoding, prefix caching, smarter quantisation and disaggregated prefill/decode are steadily lifting efficiency, while the open-model landscape, including European models, grows month over month. For German organisations, that combination is the practical path to fast, cost-controlled and sovereign AI on their own terms.