1200行代码逆袭！DeepSeek工程师开源轻量级vLLM，吞吐量逼近原版

Core Viewpoint - vLLM is a high-performance, open-source LLM inference and service engine developed by the University of California, Berkeley, aimed at enhancing inference speed and resource utilization, particularly memory efficiency, while being compatible with popular model libraries like Hugging Face [2][3]. Group 1: vLLM and Nano-vLLM - vLLM enables mainstream models like GPT, Mistral, and LLaMA to run faster and consume fewer resources through its innovative attention mechanism called PagedAttention [3]. - A lightweight implementation of vLLM, named Nano-vLLM, was developed by DeepSeek AI researcher Yu Xingkai, simplifying the code to under 1200 lines [4][7]. - Nano-vLLM has gained over 200 stars on GitHub, indicating community interest and engagement [5]. Group 2: Features of Nano-vLLM - Nano-vLLM offers three core functionalities: 1. Fast offline inference with performance comparable to vLLM [6]. 2. A readable codebase with a simplified implementation [7]. 3. An optimization suite that includes features like prefix caching, Torch compilation, and CUDA computation graphs [8]. Group 3: Benchmarking Results - Benchmark tests showed that Nano-vLLM produced the same output tokens as vLLM but took slightly longer, resulting in a throughput of 1314.65 tokens/s compared to vLLM's 1353.86 tokens/s [9][11]. - The testing configuration included using an RTX 4070 GPU, with a model size of Qwen3-0.6B, and random sampling of input and output lengths between 100 and 1024 tokens [10].