Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
summary
Describes training a 530B-parameter transformer on a DGX SuperPOD with over 3,072 A100 GPUs using Megatron-LM + DeepSpeed, achieving 502 petaFLOP/s on a 1T-parameter run and about 52% per-GPU peak throughput. Explains composing data, tensor, and pipeline parallelism, using microbatches and an interleaved pipeline schedule to shrink pipeline bubbles, and stresses practical fixes: checkpoint optimization to reduce job overhead, validating ideas on small models, and choosing model/batch splits so GPU counts can change dynamically on a shared cluster.
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chapters (11)
- 0:00Introduction
- 1:31Language model history
- 3:55GPU cluster setup
- 7:03Parallelism strategies
- 9:56Tensor parallelism
- 11:44Pipeline parallelism
- 16:42Configuring parallelism
- 18:04Megatron-LM implementation
- 19:27530B model performance
- 20:10Prompt engineering
- 20:31Lessons learned
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