Vllm pypi. 9 -- 3. 1 - a Python package on PyPI Quickstart This guide will help you quickly get started with vLLM to perform: Offline batched inference Online serving using OpenAI-compatible server Prerequisites OS: Linux Python: 3. 10. Choose the CUDA and PyTorch versions that suit your GPU and Python environment. py 360-408 Manual Wheel Installation # Install from PyPI (latest release) pip install vllm # Install specific version pip install vllm==0. 1. Jun 19, 2023 · vLLM is a fast and easy-to-use library for LLM inference and serving. It's recommended to use uv, a very fast Python environment manager, to create and manage Python environments. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache Optimized CUDA A high-throughput and memory-efficient inference and serving engine for LLMs - 0. Please . 13 Installation If you are using NVIDIA GPUs, you can install vLLM using pip directly. 8 pip install vllm+cu126 # For CUDA 12. 6. py 298-408 setup. 6 Docker Installation vLLM provides multi-stage Docker builds optimized for Jan 15, 2025 · vLLM is a fast and easy-to-use library for LLM inference and serving. A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm Sep 13, 2025 · Sources: setup. 0 # Install with specific CUDA version pip install vllm+cu118 # For CUDA 11. Learn how to install vLLM, a Python library for large-scale language modeling, with pip or from source. py 608-638 setup. py 286-296 setup. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. pwxk phj enwg duyoty uynna trgt maadf ychupor qiwsg tqwbkond