5 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
Yinsicheng Jiang, Yao Fu, Yeqi Huang +13
The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory re…
WaferLLM: Large Language Model Inference at Wafer Scale
Congjie He, Yeqi Huang, Pei Mu +5
Emerging AI accelerators increasingly adopt wafer-scale manufacturing technologies, integrating hundreds of thousands of AI cores in a mesh architecture with large distributed on-c…
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
Yinsicheng Jiang, Yao Fu, Yeqi Huang +13
The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory re…
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
Hanjing Wang, Man-Kit Sit, Congjie He +5
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transf…