emerging-techniques-moe-training

Topics in the source

• MoE Training: Mixture of Experts
• When to Use This Skill
• Installation
• Quick Start
• Basic MoE Architecture
• DeepSpeed MoE Training
• Core Concepts
• 1. MoE Architecture

Use MoE Training when you need to: Train larger models with limited compute (5× cost reduction vs dense models) Scale model capacity without proportional compute increase Achieve better performance per compute budget than dense models Specialize experts for different domains/tasks/languages Reduce inference latency with sparse activation (only 13B/47B params active in Mixtral) Implement SOTA models like Mixtral 8x7B, DeepSeek-V3, Switch Transformers Notable MoE Models: Mixtral 8x7B (Mistral AI), DeepSeek-V3, Switch Transformers (Google), GLaM (Google), NLLB-MoE (Meta) Key Components: Experts: Multiple specialized FFN networks (typically 8-128) Router/Gate: Learned network that selects which experts to use Top-k Routing: Activate only k experts per token (k=1 or k=2) Load Balancing: Ensure even expert utilization Top-1 Routing (Switch Transformer): Top-2 Routing (Mixtral): Expert Choice Routing: Auxiliary Loss: Router Z-Loss (Stability): DeepSpeed MoE Tutorial: https://www.deepspeed.ai/tutorials/mixture-of-experts-nlg/ Mixtral Paper: https://arxiv.org/abs/2401.04088 Switch Transformers: https://arxiv.org/abs/2101.03961 HuggingFace MoE Guide: https://huggingface.co/blog/moe NVIDIA Mo

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