vLLM
A high-throughput inference engine for serving open models efficiently in production.
vLLM is a serving engine built for throughput. Techniques like paged attention and continuous batching let it serve far more concurrent requests per GPU than a naive setup, with an OpenAI-compatible API.
It has become the default open-source choice for production self-hosted inference, and most new open models target it early.
Where it fits
The right engine when you self-host a model under real concurrency and need to use GPU capacity efficiently.
Where it does not fit
More setup than warranted for a single-user local model: for that, a one-command runtime is the better tool.
Alternatives to vLLM
The closest options on the map, and where each fits instead.
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Text Generation Inference Inference engine
A fit for production self-hosted serving, especially for teams already deep in the Hugging Face ecosystem.
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SGLang Inference engine
A fit for high-throughput self-hosted serving, especially with structured output or heavy shared-prefix workloads.
Unlike vLLM Maturity: growing
How it compares
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Where Text Generation Inference does not fit
For a single-user local model it is heavier than a simple runtime, and vLLM has broader community momentum.
Pairs with
Commonly used alongside it.
