Efficient Large-Scale Language Model Training on GPU ...
would require approximately 288 years with a single V100 NVIDIA GPU). This calls for parallelism. Data-parallel scale-out usually works well, but suffers from two limitations: a) beyond a point, the per-GPU batch size becomes too small, reducing GPU utilization and increasing communication cost, and b) the maximum number
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