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URL https://arxiv · arXiv 2507.14392

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cs.DC 1

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2025 1

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CONDITIONAL 1

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Understanding and Improving Communication Performance in Multi-node LLM Inference

cs.DC · 2025-11-12 · conditional · novelty 5.0

Performance analysis of multi-node LLM inference identifies all-reduce bottlenecks and introduces NVRAR hierarchical all-reduce achieving 1.9-3.6x lower latency than NCCL and up to 1.72x end-to-end batch latency reduction for Llama 3.1 405B in decode-heavy tensor-parallel workloads.

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  • Understanding and Improving Communication Performance in Multi-node LLM Inference cs.DC · 2025-11-12 · conditional · none · ref 18

    Performance analysis of multi-node LLM inference identifies all-reduce bottlenecks and introduces NVRAR hierarchical all-reduce achieving 1.9-3.6x lower latency than NCCL and up to 1.72x end-to-end batch latency reduction for Llama 3.1 405B in decode-heavy tensor-parallel workloads.