Posts by Ke Wang
QuickReduce: Up to 3x Faster All-reduce for vLLM and SGLang
- 26 August 2025
Advancements in large-scale language models (LLMs) have led to significant performance breakthroughs across various domains, especially in natural language processing. LLMs typically consist of billions of parameters, resulting in substantial computational, storage, and deployment challenges. Inter-GPU communication overhead often emerges as a key bottleneck limiting overall system performance. In tensor-parallel setups, every layer requires frequent all-reduce operations—synchronizing large amounts of data across GPUs. This introduces significant latency and strains interconnect bandwidth.