327  vllm(1) - Kailash Tool Reference

327.1 vllm

vllm - vLLM — high-throughput, memory-efficient LLM inference and serving engine (native at the pin; plan Appendix A listed bespoke).

Layer OPS
Category o-02-compute-serving-infrastructure
Safety class safe
Licence Apache-2.0
Upstream github.com/vllm-project/vllm

327.1.1 NAME

vllm - vLLM — high-throughput, memory-efficient LLM inference and serving engine (native at the pin; plan Appendix A listed bespoke).

327.1.2 SYNOPSIS & usage

Derived from the upstream README at generation time; verify against --help where the tool is installed.

uv pip install vllm
vllm download-kernels  # precompiled kernels for faster startup
@inproceedings{kwon2023efficient,
  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
  year={2023}
}

327.1.3 DESCRIPTION (upstream)

Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key

327.1.4 SEE ALSO

ref: pin - verified 2026-10-14