7 citations · 13 across the 6 of their papers we have counts for
7 papers
BatchGen: An Architecture for Scalable and Efficient Batch Inference
Tairan Xu, Leyang Xue, Zhan Lu +7
Batch inference has become a central mode of AI computation, yet existing inference engines still rely on execution models designed for interactive serving. When scaled to millions…
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…
HybridServe: Efficient Serving of Large AI Models with Confidence-Based Cascade Routing
Leyang Xue, Yao Fu, Luo Mai +1
Giant Deep Neural Networks (DNNs), have become indispensable for accurate and robust support of large-scale cloud based AI services. However, serving giant DNNs is prohibitively ex…
MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
Tairan Xu, Leyang Xue, Zhan Lu +2
This paper presents MoE-Gen, a high-throughput MoE inference system optimized for single-GPU execution. Existing inference systems rely on model-based or continuous batching strate…
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…