A DeepSeek developer has released nano-vLLM, a lightweight open-source AI inference engine written in just 1,200 lines of Python, offering surprising performance that rivals vLLM.
It features key optimizations like prefix caching, tensor parallelism, and CUDA graphs, making it fast, efficient, and easy to understand for developers and AI learners.
Designed for offline LLM inference, nano-vLLM delivers impressive speed using minimal GPU resources, and its clean, readable code is quickly gaining attention across the AI and open-source communities.