English(EN) 28 TPS on Qwen2.5-7B across two separate cloud regions over public WAN using speculative decoding + CUDA Graphs [P]

ShardFlow框架使用推测解码在Qwen2.5-7B上实现28 TPS

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-23 12:30

一位开发者构建了ShardFlow，一个分布式LLM推理框架，该框架将模型分布在多台机器上，并使用推测解码来缓解WAN延迟。在Qwen2.5-7B上的基准测试显示吞吐量显著提高，使用推测解码和CUDA Graphs达到了28.10 TPS，而基线为4.92 TPS。该框架还采用了零拷贝Rust TCP中继和元设备模型切片等技术来优化性能。 AI

影响展示了分布式LLM推理的显著性能提升，可能能够更有效地在广域网上部署大型模型。

排序理由该项目详细介绍了用于分布式LLM推理的推测解码和CUDA Graphs的新颖实现，并展示了基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该项目详细介绍了用于分布式LLM推理的推测解码和CUDA Graphs的新颖实现，并展示了基准测试结果。[lever_c_demoted from research: ic=1 ai=1.0]
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r/MachineLearning TIER_1 English(EN) · /u/katua_bkl · 2026-08-23 12:30

Qwen2.5-7B 在两个独立的云区域通过公共 WAN 使用推测解码 + CUDA Graphs 实现 28 TPS [P]

<div class="md">been building ShardFlow for the past few months, a distributed LLM inference framework that splits any HuggingFace transformer across N GPU machines and uses neural speculative decoding to deal with WAN latency. the setup f…

本文如何被排名

报道来源 [1]

Qwen2.5-7B 在两个独立的云区域通过公共 WAN 使用推测解码 + CUDA Graphs 实现 28 TPS [P]

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