1 citations · 1 across the 3 of their papers we have counts for
5 papers
SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference
Yihao Zhao, Jiadun Chen, Peng Sun +3
Large language models (LLMs) with different architectures and sizes have been developed. Serving each LLM with dedicated GPUs leads to resource waste and service inefficiency due t…
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…
LoongTrain: Efficient Training of Long-Sequence LLMs with Head-Context Parallelism
Diandian Gu, Peng Sun, Qinghao Hu +11
Efficiently training LLMs with long sequences is important yet challenged by the massive computation and memory requirements. Sequence parallelism has been proposed to tackle these…
LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism
Bingyang Wu, Shengyu Liu, Yinmin Zhong +3
The context window of large language models (LLMs) is rapidly increasing, leading to a huge variance in resource usage between different requests as well as between different phase…
InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding
Qiaoling Chen, Diandian Gu, Guoteng Wang +8
Large language models (LLMs) with long sequences begin to power more and more fundamentally new applications we use every day. Existing methods for long-sequence LLM training are n…