vLLM

What is vLLM?

vLLM is an open-source inference and serving engine for large language models. According to the docs, it is built around a memory management technique called PagedAttention, which enables high throughput and efficient use of GPU memory. The project repository on GitHub shows 87,346 stars, indicating wide community adoption. The engine is designed for both single-model deployment and multi-model serving, with support for continuous batching and streaming outputs. vLLM handles model loading, KV-cache management, and request scheduling internally, so users do not need to write custom serving code. The official website at vllm.ai provides documentation on model compatibility, installation, and API endpoints.

Who is vLLM for?

vLLM targets developers and teams who need to run LLM inference at scale. Use cases include production API endpoints, internal chatbots, batch processing pipelines, and research workloads. The docs describe support for common model architectures such as Llama, Mistral, and Falcon, making it applicable for teams using open-weight models. vLLM is also used in larger deployment stacks as a backend for frameworks like LangChain and LlamaIndex. Because it is free and self-hosted, it appeals to organizations that want to avoid per-token API costs while maintaining control over latency and data privacy. The tool requires familiarity with Python and command-line operations, and assumes access to GPU hardware. It is not designed for non-technical end users.

How much does vLLM cost?

The pricing model for vLLM is free. The pricing page lists a starting cost of $0 per month. As an open-source project under the Apache 2.0 license, there are no licensing fees. Users bear only their own infrastructure costs, such as GPU cloud instances or on‑premises servers. The repository does not mention any paid tiers or enterprise add-ons. Community support is available through GitHub issues and discussions; no paid support plans are advertised. This makes vLLM a cost-efficient choice for teams that have existing GPU capacity or are comfortable managing their own compute.