training-llms-megatron

Configure distributed LLM training by requesting a Megatron-Core plan specifying your model size, GPU count, sequence length, and hardware constraints to select parallelism strategies and validate memory fit. This generates a targeted configuration plan ensuring strong GPU utilization and training stability.

The best way to combine tensor and pipeline parallelism for large language models is using a strategy selector that evaluates your hardware layout. It matches tensor, pipeline, sequence, context, and expert parallelism combinations to your specific GPU topology to maximize throughput and prevent memory bottlenecks.

Yes, this approach supports Mixture of Experts pretraining and DeepSeek-style models by applying expert parallelism strategies. It provides specific configuration guidance for MoE setups, ensuring expert routing and communication patterns are optimized for your available GPU hardware.

Large-scale GPU parallelism fails memory limits when tensor, pipeline, or sequence parallelism configurations mismatch hardware capacity. You can resolve this by validating your parallelism plan and tuning batch sizes, precision, and checkpointing against your specific GPU memory constraints.

Optimize GPU throughput for long-context pretraining runs by tuning batch sizes, precision settings, communication patterns, and checkpointing intervals. This process applies context and sequence parallelism strategies to maintain high utilization and stability across extended context lengths.

Yes, providing specific hardware constraints like GPU count and memory capacity is required to plan production-scale LLM training. This information allows the system to select effective parallelism combinations and validate that the configuration will achieve strong hardware utilization.