15 citations · 27 across the 31 of their papers we have counts for
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cs.DC2024★ 1 cited
MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
Shiyi Cao, Shu Liu, Tyler Griggs +6
Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of comp…
cs.DC2024★ 1 cited
NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference
Xuanlin Jiang, Yang Zhou, Shiyi Cao +2
Online LLM inference powers many exciting applications such as intelligent chatbots and autonomous agents. Modern LLM inference engines widely rely on request batching to improve i…
cs.DC2024★ 1 cited
ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving
Yifan Qiao, Shu Anzai, Shan Yu +10
Large language model (LLM) serving demands low latency and high throughput, but high load variability makes it challenging to achieve high GPU utilization. In this paper, we identi…