CommFuse: New Technique to Eliminate Tail Latency in Distributed LLM Training

A research paper on arXiv (2604.24013) introduces CommFuse, a novel communication-computation overlap technique designed to eliminate tail latency in distributed training of large language models. As LLM sizes grow, computational workloads are partitioned across accelerators like GPUs, TPUs, and NPUs, but parallelization strategies cause substantial data communication overhead that hinders efficiency. Existing data slicing-based solutions suffer from tail latency. CommFuse replaces conventional collective operations of reduce-scatter and all-gather with decomposed and fused communication patterns to mitigate the communication bottleneck in tensor parallelism and data parallelism for both training and inference. The paper was announced as a cross-type submission on arXiv.