SiPipe claims 1.6x to 2.1x throughput gains over vLLM for pipeline-parallel LLM inference by using idle CPUs for sampling, double-buffered CUDA graphs, and structure-aware transmission.
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SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference
SiPipe claims 1.6x to 2.1x throughput gains over vLLM for pipeline-parallel LLM inference by using idle CPUs for sampling, double-buffered CUDA graphs, and structure-aware transmission.