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This skill provides comprehensive guidance on training Mixture of Experts (MoE) models, including architectures like Mixtral and DeepSeek-V3. It details techniques for achieving large-scale model capacity with significantly reduced computational costs (e.g., 5x reduction vs. dense models). Topics covered include top-k routing, load balancing, expert parallelism, and integration with frameworks like DeepSpeed and HuggingFace for efficient, resource-constrained training.

Provides production-ready Megatron-Core workflows for training 2B–462B parameter LLMs with tensor/pipeline/expert/context parallelism, maximizing GPU efficiency on H100 clusters.

Expert guidance for implementing distributed training with DeepSpeed, covering ZeRO optimization stages, pipeline parallelism, mixed precision (FP16/BF16/FP8), and I/O optimizations like DeepNVMe. Helps with debugging, API usage, and best practices for large-scale model training on NVIDIA GPUs.

Megatron-Core is a production-ready framework for training large language models ranging from 2B to 462B parameters. It utilizes advanced parallelism strategies like tensor, pipeline, and expert parallelism to maximize GPU efficiency on NVIDIA H100 hardware. Ideal for scaling models beyond 1B parameters with high MFU performance.

Learn to train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. This skill covers sparse architectures like Mixtral 8x7B, reducing compute costs by 5x compared to dense models. It includes routing mechanisms, load balancing, expert parallelism, and inference optimization for scaling large-scale AI models efficiently.