<ul><li>vLLM is an end-to-end serving system with a FastAPI frontend and a GPU-based inference engine.</li><li>The vLLM engine is written in Python and C++/CUDA code and employs control-related components such as the scheduler and block manager.</li><li>Kernel-level optimization techniques are used to optimize memory access patterns introduced by PagedAttention.</li><li>vLLM implements various decoding algorithms using methods like fork, append, and free, and supports parallel sampling, beam search, and prefix sharing.</li></ul>

How vLLM Implements Decoding Algorithms

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