6 citations · 9 across the 8 of their papers we have counts for
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cs.DC2026
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
Qiaoling Chen, Zhisheng Ye, Tian Tang +7
Batch inference for agentic workloads stresses the GPU key-value (KV) cache in a sustained and cumulative manner, often causing severe throughput degradation well before memory cap…
cs.DC2025★ 1 cited
SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
Qiaoling Chen, Shenggui Li, Wei Gao +3
In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities, driving advancements in real-world applications. However, training LLMs on increasingly long i…
cs.DC2022★ 1 cited
Elixir: Train a Large Language Model on a Small GPU Cluster
Haichen Huang, Jiarui Fang, Hongxin Liu +2
In recent years, large language models have achieved great success due to their unprecedented size. However, training these models poses a challenge for most researchers as it requ…