4 papers
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
Wenxiang Lin, Xinglin Pan, Ruibo Fan +2
Communication has emerged as a critical bottleneck in the distributed training of large language models (LLMs). While numerous approaches have been proposed to reduce communication…
ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
Ruibo Fan, Xiangrui Yu, Xinglin Pan +5
Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approach…
Dissecting Outlier Dynamics in LLM NVFP4 Pretraining
Peijie Dong, Ruibo Fan, Yuechen Tao +11
Training large language models using 4-bit arithmetic enhances throughput and memory efficiency. Yet, the limited dynamic range of FP4 increases sensitivity to outliers. While NVFP…
Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis
Weile Luo, Ruibo Fan, Zeyu Li +4
This study presents a comprehensive multi-level analysis of the NVIDIA Hopper GPU architecture, focusing on its performance characteristics and novel features. We benchmark Hopper'…