3 citations · 5 across the 8 of their papers we have counts for
11 papers
Tropical: Enhancing SLO Attainment in Disaggregated LLM Serving via SLO-Aware Multiplexing
Jinming Ma, Jiefei Chen, Xiuhong Li +5
To guarantee service quality in transformer based large language model (LLM) serving, it is essential to meet the latency constraints of both the prefill phase (measured by Time-to…
NanoCP: Taming Load Imbalance in Data-Expert Parallel Decoding under Variable-Length Workloads
Jiefei Chen, Binbin Lin, Jinming Ma +9
Modern serving systems for Mixture-of-Experts (MoE) models adopt hybrid data-expert parallelism: expert parallelism (EP) shards experts across GPUs to scale capacity, while data pa…
Zeppelin: Balancing Variable-length Workloads in Data Parallel Large Model Training
Chang Chen, Tiancheng Chen, Jiangfei Duan +7
Training large language models (LLMs) with increasingly long and varying sequence lengths introduces severe load imbalance challenges in large-scale data-parallel training. Recent…
MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design
Haojie Duanmu, Xiuhong Li, Zhihang Yuan +4
Mixture-of-Experts (MoE) models face deployment challenges due to their large parameter counts and computational demands. We explore quantization for MoE models and highlight two k…
TrainMover: An Interruption-Resilient Runtime for ML Training
ChonLam Lao, Jiaqi Gao, Jiamin Cao +13
Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solutions like checkpoint-restart or runtime r…
Efficient Training of Large Language Models on Distributed Infrastructures: A Survey
Jiangfei Duan, Shuo Zhang, Zerui Wang +13
Large Language Models (LLMs) like GPT and LLaMA are revolutionizing the AI industry with their sophisticated capabilities. Training these models requires vast GPU clusters and sign…
SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
Qianchao Zhu, Jiangfei Duan, Chang Chen +6
Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT)…
SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
Haojie Duanmu, Zhihang Yuan, Xiuhong Li +3
Large language models (LLMs) can now handle longer sequences of tokens, enabling complex tasks like book understanding and generating lengthy novels. However, the key-value (KV) ca…
MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving
Jiangfei Duan, Runyu Lu, Haojie Duanmu +5
Large language models (LLMs) have demonstrated remarkable performance, and organizations are racing to serve LLMs of varying sizes as endpoints for use-cases like chat, programming…
Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances
Jiangfei Duan, Ziang Song, Xupeng Miao +5
Deep neural networks (DNNs) are becoming progressively large and costly to train. This paper aims to reduce DNN training costs by leveraging preemptible instances on modern clouds,…
SpotServe: Serving Generative Large Language Models on Preemptible Instances
Xupeng Miao, Chunan Shi, Jiangfei Duan +4
The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary cost for…