3 papers
cs.DC2025
Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
Guoliang He, Youhe Jiang, Wencong Xiao +8
The scaling law for large language models (LLMs) depicts that the path towards machine intelligence necessitates training at large scale. Thus, companies continuously build large-s…
cs.DC2025
MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
Ruidong Zhu, Ziheng Jiang, Chao Jin +17
Mixture-of-Experts (MoE) showcases tremendous potential to scale large language models (LLMs) with enhanced performance and reduced computational complexity. However, its sparsely…
stat.AP2025
Performance Evaluation of Large Language Models in Statistical Programming
Xinyi Song, Kexin Xie, Lina Lee +10
The programming capabilities of large language models (LLMs) have revolutionized automatic code generation and opened new avenues for automatic statistical analysis. However, the v…