training-llms-megatron

Configure distributed parallelism for large language model training by aligning tensor, pipeline, sequence, context, and expert parallelism degrees with your GPU topology. Matching these parallelism strategies to your hardware ensures high GPU utilization and stable convergence during pretraining or fine-tuning.

The best way to train a 70B LLM on H100 GPUs without out-of-memory failures is configuring tensor, pipeline, and context parallelism alongside FP8 optimizations. This approach sustains high Model FLOP Utilization while fitting large models within tight GPU memory limits.

Use FP8 and Flash-Attention to improve LLM training throughput by integrating transformer-engine and setting the corresponding batch and micro-batch parameters. These optimizations target sustained Model FLOP Utilization and stable convergence during large-scale distributed training.

Yes, you can use mixture-of-experts parallelism across multiple nodes for LLM pretraining. This distributed parallelism strategy routes expert computations across your hardware topology, enabling scalable training for models up to hundreds of billions of parameters.

LLM training throughput regresses and low MFU occurs when parallelism degrees mismatch your hardware topology or batch parameters are suboptimal. Troubleshoot by realigning tensor and pipeline parallelism with NVLink or InfiniBand topology and adjusting micro-batch sizes.