MegaScale-Omni delivers 1.27x-7.57x higher throughput for dynamic multimodal LLM training by decoupling encoder and LLM parallelism, using unified colocation, and applying adaptive workload balancing.
Adaptra: Straggler-resilient hybrid-parallel training with pipeline adaptation
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ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.
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MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
MegaScale-Omni delivers 1.27x-7.57x higher throughput for dynamic multimodal LLM training by decoupling encoder and LLM parallelism, using unified colocation, and applying adaptive workload balancing.
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ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism
ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.