vLLM

What is it?

vLLM is software for running language models efficiently for many users at once. It manages GPU memory cleverly and batches requests continuously, so a single GPU server can answer far more requests than a naive setup.

Also known as: vLLM server, vllm serve, PagedAttention, continuous batching, LLM inference engine, high throughput LLM serving, OpenAI compatible server, self-hosted LLM serving.

Technical definition

Introduced with PagedAttention (Kwon et al., 2023) and a PyTorch Foundation hosted project since 2025, vLLM stores KV cache in non-contiguous blocks to reduce fragmentation, and schedules requests with continuous batching. Common features: OpenAI-compatible HTTP server, tensor/pipeline parallelism, prefix caching, quantisation support, speculative decoding, guided decoding backends (Constrained Decoding (Guided Generation)), LoRA adapter serving, and multi-modal model support. Hardware support (NVIDIA, AMD, others), supported models and flags change frequently.

Entity type: runtime · Freshness class: volatile.

When should I use it?

Use for production-style self-hosted serving on GPUs.

Prerequisites and related concepts

Understand these first: KV Cache, Model Serving and Inference Infrastructure.

Related concepts: Local and Self-Hosted LLM Runtimes Compared, llama.cpp, Ollama, Quantization of AI Models, Speculative Decoding, GPUs and AI Accelerators, Constrained Decoding (Guided Generation).

Implementation patterns

• Run behind a gateway with auth and rate limits.
• Use tensor parallelism for models larger than one GPU.
• Enable prefix caching for shared prompts.
• Autoscale replicas with a load balancer.

Technologies and frameworks

Commonly associated: vLLM, CUDA, Ray (for multi-node), Hugging Face model hub. Names are examples of what exists, not recommendations, and projects change quickly.

Practical guide

1. Check model support and licence.
2. Size GPU memory (weights + KV cache).
3. Launch the OpenAI-compatible server.
4. Load-test with realistic traffic.
5. Add auth, monitoring (LLM Observability (Tracing, Logging and Monitoring)).

Common failure modes

• OOM from too-large max context or batch settings.
• Version/CUDA/driver mismatches.
• Unsecured open endpoint.
• Assuming every model architecture is supported.

Troubleshooting

• CUDA out of memory — Reduce max model length / GPU memory utilisation, quantise, or add GPUs.
• Server fails to start — Match vLLM, PyTorch, CUDA and driver versions; check model support list.

Contrasted with

• Ollama — Ollama prioritises easy local use; vLLM prioritises multi-user throughput.
• llama.cpp — llama.cpp excels on CPUs/consumer hardware and GGUF; vLLM targets GPU servers.

Questions this page answers

• What is vLLM?
• How do I serve an LLM with vLLM?
• vLLM vs Ollama vs llama.cpp?
• What is continuous batching?
• How does PagedAttention work?

Typical goals: serve an open model to many users; choose an inference engine.

Sources and verification

Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.

Freshness class: volatile — Products, versions, protocols, benchmarks, leaderboards, regulations and dates. Verify against the primary source before relying on specifics; re-review every 3 months.

Time-sensitive claims to verify before relying on them:

• Supported hardware, models, features and flags.
• Project governance and hosting.

Canonical sources (unverified leads):

• Kwon et al., Efficient Memory Management for LLM Serving with PagedAttention (2023) — arXiv:2309.06180
• vLLM documentation — docs.vllm.ai (verify)

Related concepts