With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop (SPD), to reduce communication overheads in tensor parallelism by selectively dropping synchronization on attention outputs. In detail, we first propose a block design that allows execution to proceed without communication through SPD. Second, we apply different SPD strategies to attention blocks based on their sensitivity to the model accuracy. The proposed methods effectively alleviate communication bottlenecks while minimizing accuracy degradation during LLM inference, offering a scalable solution for diverse distributed environments: SPD offered about 20% overall inference latency reduction with < 1% accuracy regression for LLaMA2-70B inference over 8 GPUs.
@article{arxiv.2502.20727,
title = {SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models},
author = {Han-Byul Kim and Duc Hoang and Arnav Kundu and Mohammad Samragh and Minsik Cho},
journal= {arXiv preprint arXiv:2502.20727},
year = {2025}
}
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International Conference on Machine Learning (ICML) 2025