7 citations · 15 across the 7 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…
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
Leyang Xue, Yao Fu, Zhan Lu +2
This paper presents MoE-Infinity, an efficient MoE inference system designed for personal machines with limited GPU memory capacity. The key idea for MoE-Infinity is that on person…
ServerlessLLM: Low-Latency Serverless Inference for Large Language Models
Yao Fu, Leyang Xue, Yeqi Huang +4
This paper presents ServerlessLLM, a distributed system designed to support low-latency serverless inference for Large Language Models (LLMs). By harnessing the substantial near-GP…
Critical Data Size of Language Models from a Grokking Perspective
Xuekai Zhu, Yao Fu, Bowen Zhou +1
We explore the critical data size in language models, a threshold that marks a fundamental shift from quick memorization to slow generalization. We formalize the phase transition u…