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The report documents an optimization the team tried and abandoned: overlapping communication with computation slows the overlapped kernels by more than the overhead it hides, because the two compete for the same batch of GPU resources.
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10/14/2026
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UnverifiedIn single-batch scenarios where batch size is 1, the arithmetic intensity of matrix multiplication drops significantly, memory bandwidth becomes the primary bottleneck, and the efficiency of traditional GPU architectures optimized for high throughput decreases markedly.76% similarUnverifiedIn collective communication operations such as all-reduce, a network anomaly on a single GPU is sufficient to slow down the entire training job.75% similarUnverifiedGPU的SIMD架构在推理阶段面临结构性错配，推理时batch size较小导致算力利用率不足，内存带宽成为核心瓶颈74% similarVerifiedCPU-GPU hybrid inference (offloading) introduces a significant drop in inference speed.73% similarUnverifiedThe gains from speculative decoding are diluted in large-batch scenarios, as GPU compute capacity tends to be saturated in such cases.72% similar
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