The high-throughput inference engine that serves open LLMs at production scale.
vLLM is the open-source serving engine behind a huge share of self-hosted LLM deployments. PagedAttention lets it batch thousands of requests with near-linear throughput. If you run Llama, Qwen, or Mistral in production, you've probably already met it.
Who it's for: ML engineers and platforms teams self-hosting open-weight models.
Memory-efficient attention paging multiplies concurrent throughput.
Incoming requests are batched on the fly for maximal GPU use.
Exposes an OpenAI-style server so clients need zero changes.
Supports AWQ, GPTQ, and FP8 to fit more on less VRAM.
vLLM is the default choice for serving open LLMs at scale in 2026. If you're hosting models and not using it, you're leaving GPU utilization — and money — on the table.