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cs.DC2025
SYMI: Efficient Mixture-of-Experts Training via Model and Optimizer State Decoupling
Athinagoras Skiadopoulos, Mark Zhao, Swapnil Gandhi +3
Mixture-of-Experts (MoE) models have become a widely-adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training,…
cs.DC2025
Strata: Hierarchical Context Caching for Long Context Language Model Serving
Zhiqiang Xie, Ziyi Xu, Mark Zhao +5
Large Language Models (LLMs) with expanding context windows face significant performance hurdles. While caching key-value (KV) states is critical for avoiding redundant computation…
cs.DC2024
ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
Swapnil Gandhi, Mark Zhao, Athinagoras Skiadopoulos +1
Training large Deep Neural Network (DNN) models requires thousands of GPUs over the course of several days or weeks. At this scale, failures are frequent and can have a big impact…