From the 1 of 7 linked papers with an AI index.
7 papers
Zellige: Moldable Sequence Placement for Mixed Image-Video DiT Training
Guangyu Xiang, Xueze Kang, Minwei Zhao +4
High-quality video generation requires training Diffusion Transformers (DiTs) jointly on image and video data, posing a mixed-length sequence training problem across GPUs. Existing…
Xema: Efficient Diffusion Serving through Fine-Grained Memory Management and Auto-Configuration
Xueze Kang, Guangyu Xiang, Suyi Li +4
Xema is a system that reduces GPU memory usage for diffusion model serving by analyzing tensor lifetimes to apply targeted memory mitigation and by planning parallelism and concurr…
KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding
Guangyu Xiang, Xueze Kang, Lin Zhang +4
LLM serving is increasingly dominated by long and dynamic decode workloads from agents, reasoning models, and extended conversations. When bursty long-context demand exceeds deploy…
On the Spectral Flattening of Quantized Embeddings
Junlin Huang, Wenyi Fang, Zhenheng Tang +5
Training Large Language Models (LLMs) at ultra-low precision is critically impeded by instability rooted in the conflict between discrete quantization constraints and the intrinsic…
ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
Xueze Kang, Guangyu Xiang, Yuxin Wang +16
Large-scale LLM pretraining now runs across -- accelerators, making failures routine and elasticity mandatory. We posit that an elastic-native training system must join…
BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems
Yuxin Wang, Yuhan Chen, Zeyu Li +11
Serving systems for Large Language Models (LLMs) are often optimized to improve quality of service (QoS) and throughput. However, due to the lack of open-source LLM serving workloa…