TL;DR — vLLM is a high-throughput LLM inference engine. Two ideas make it fast: PagedAttention (treat the KV cache like paged virtual memory, killing fragmentation) and continuous batching (slot new requests into the running batch instead of waiting). Together they deliver up to ~24× the throughput of naive serving. Run it as a Python library or as a drop-in OpenAI-compatible server.
What it is
vLLM is an open-source library and server for fast LLM inference and serving. It loads a model (Hugging Face weights, many architectures) and serves completions at high throughput, exposing an OpenAI-compatible API so existing clients just point at it. Its re-architected V1 engine is the current core. In the AI Native landscape it's in AI Native Infra › Workload Runtime — and it's the engine inside many higher-level serving stacks.
Why it exists
Naive LLM serving leaves GPUs badly underused. The KV cache (per-request attention memory) is allocated in big contiguous chunks, so memory fragments and you can't fit many concurrent requests; and static batching makes finished requests wait for the slowest in the batch. vLLM attacks both — at the memory layer and the scheduler layer — so the same GPU serves far more traffic.
PagedAttention
The KV cache is split into small fixed-size blocks (pages) allocated on demand, like OS virtual memory — instead of one contiguous block per sequence. No pre-reserving for max length, almost no fragmentation, and blocks can even be shared across requests (prefix caching). The result: bigger batches in the same GPU memory.
Fig 1 — Paging the KV cache removes the reserved-but-unused waste.
Continuous batching
Rather than fixing a batch and waiting for every sequence to finish, vLLM's scheduler runs a rolling batch: the moment one request completes a token-generation slot, a waiting request takes its place. The GPU stays saturated, p50 latency drops, and bursty production traffic is absorbed smoothly — reportedly up to ~23× throughput vs static batching.
More that's in the box
- OpenAI-compatible server —
/v1/chat/completions&/v1/completions, streaming. - Tensor / pipeline parallelism — shard big models across multiple GPUs.
- Quantization — FP8/INT8/AWQ/GPTQ to fit more in memory.
- Prefix caching & chunked prefill — reuse shared prompt prefixes; interleave prefill with decode for steadier latency.
- Speculative decoding & LoRA — faster decode; serve many adapters on one base model.
Quick start
Install, serve a model with one command, then call it like OpenAI:
pip install vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct # OpenAI server on :8000
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"meta-llama/Llama-3.1-8B-Instruct","messages":[{"role":"user","content":"hi"}]}'Any OpenAI client works by setting base_url to the vLLM server. For multi-GPU, add --tensor-parallel-size N.
When to use, when to skip
Use it when you self-host LLMs and care about throughput and GPU cost — high-concurrency inference, batch generation, or as the engine behind a serving platform. It's the default high-performance choice for production OSS inference.
Skip it for casual local use on a laptop — Ollama is friendlier. For full cluster lifecycle (autoscaling, canary, multi-model) put vLLM under KServe rather than running it bare. SGLang and TGI are close alternatives worth benchmarking for your workload.
gpu-memory-utilization, max-num-seqs, and chunked-prefill settings change the balance. And model + GPU memory must actually fit — quantize or shard with tensor parallelism for big models.vs the alternatives
| Tool | Best for | Trade-off |
|---|---|---|
| vLLM | High-throughput self-hosted LLM serving | You operate the GPU box/cluster |
| SGLang | Structured/agentic workloads, RadixAttention | Benchmark per workload |
| Ollama | Easy local/dev model running | Not built for high concurrency |
| KServe | Cluster serving platform (wraps vLLM) | More infra to run |
References
- vLLM docs — official documentation.
- Quickstart — serve a model fast.
- vllm-project/vllm — source.
- PagedAttention paper — the core idea.
Extra reads
- PagedAttention & continuous batching explained — deep dive.
- LLM serving optimization on H100 — tuning in practice.
- A quick guide to vLLM — intro overview.
Verified against the official vLLM docs (docs.vllm.ai), May 2026.