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Developer Tool • Jun 20, 2026
vLLM
High-throughput inference engine for serving open-weight language models.
What it helps you do
- Use this when self-hosted inference needs higher throughput, batching, OpenAI-compatible endpoints, and efficient serving for open-weight models.
Best fit users
- ML infrastructure engineers, AI platform teams, backend teams serving models at scale, and companies controlling their own inference stack.
What to know first
- Linux/server operations, GPU deployment knowledge, model selection experience, and understanding of latency, throughput, batching, and memory tradeoffs.
Environment needed
- Linux server environment, Python, compatible GPU/CUDA stack for common deployments, sufficient VRAM for the selected model, and production networking/load-balancing when serving users.
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