vLLM is an open-source library designed for efficient inference and serving of large language models (LLMs). It provides state-of-the-art throughput and memory optimization through techniques such as PagedAttention, continuous batching, and CUDA/HIP graph execution. Key features include support for various quantizations (e.g., GPTQ, AWQ, FP8), speculative decoding, and seamless integration with Hugging Face models. It is ideal for developers and researchers needing scalable LLM deployment in production environments, with applications in AI-powered applications, model serving, and distributed inference across multiple hardware platforms.
vLLM
A high-throughput and memory-efficient inference and serving engine for large language models (LLMs), offering fast, scalable deployment with features like Page
Introduction
Information
- Websitevllm.ai
- Published date2025/11/16
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