1 citations · 1 across the 4 of their papers we have counts for
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TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval
Chien-Yu Lin, Keisuke Kamahori, Yiyu Liu +11
Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage. Modern RAG pipelines rely o…
GENSERVE: Efficient Co-Serving of Heterogeneous Diffusion Model Workloads
Fanjiang Ye, Zhangke Li, Xinrui Zhong +10
Diffusion models have emerged as the prevailing approach for text-to-image (T2I) and text-to-video (T2V) generation, yet production platforms must increasingly serve both modalitie…
FAST: An Efficient Scheduler for All-to-All GPU Communication
Yiran Lei, Dongjoo Lee, Liangyu Zhao +9
All-to-All(v) communication is a critical primitive in modern machine learning workloads, particularly mixture-of-experts (MoE) models. Unfortunately, efficient scheduling is chall…
PolyServe: Efficient Multi-SLO Serving at Scale
Kan Zhu, Haiyang Shi, Le Xu +4
Advances in Large Language Models (LLMs) have led to a surge of LLM-powered applications. These applications have diverse token-generation latency requirements. As a result, simply…
NanoFlow: Towards Optimal Large Language Model Serving Throughput
Kan Zhu, Yufei Gao, Yilong Zhao +13
Large Language Models (LLMs) have resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. C…
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
Zihao Ye, Lequn Chen, Ruihang Lai +8
Transformers, driven by attention mechanisms, form the foundation of large language models (LLMs). As these models scale up, efficient GPU attention kernels become essential for hi…