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1 points•0 comments•4d ago•by matt_d•docs.google.com ↗

vLLM-Compile: Bringing Compiler Optimizations to LLM Inference

The material covers vLLM-compile, a project that integrates Torch's torch.compile with the vLLM inference engine to automate kernel generation and graph-level optimizations for faster, more portable LLM execution. It explains piecewise CUDA graphs and custom fusion/transformation passes—such as Attention with Quant FP8 or FP4, RMSNorm with Quant, and SiLU-Mul with Quant—aimed at improving end-to-end performance, with reported improvements up to 11% in some cases, though not all operators are supported by CUDAGraphs. Additional goals include reducing startup and compilation time and applying optimizations independently of model definitions, as well as exploring vLLM IR to separate semantics from implementation.

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