Researchers have detailed their process for training a 1 trillion parameter Mixture-of-Experts (MoE) model on the JUPITER supercomputer, utilizing 384 NVIDIA GH200 GPUs. They achieved approximately 1,300 tokens/second per GPU by implementing a combination of Fully Sharded Data Parallelism (FSDP) and Expert Parallelism (EP), along with advanced memory optimization techniques. Key strategies included full activation checkpointing, offloading activations to Grace CPUs, and using the Muon optimizer to reduce memory overhead and communication bottlenecks. AI
IMPACT Demonstrates advanced infrastructure scaling and optimization techniques for training trillion-parameter models, potentially accelerating the development of larger and more capable AI systems.
RANK_REASON The item details novel infrastructure and optimization techniques for training a large-scale Mixture-of-Experts model, which is a significant research contribution in the field of large model training. [lever_c_demoted from research: ic=1 ai=1.0]
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