Mixture of experts

英文原文文件：moe.md

计算解释

见英文原文文件。本中文镜像只同步阅读卡链接，避免引入英文源文件中没有的额外论断。

支撑阅读卡

• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (2017, sparse_memory_efficient_scaling)
• GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2020, sparse_memory_efficient_scaling)
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (2021, sparse_memory_efficient_scaling)
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021, sparse_memory_efficient_scaling)
• Mixtral of Experts (2024, sparse_memory_efficient_scaling)
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (2024, sparse_memory_efficient_scaling)
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025, inference_time_compute_post_training)
• Qwen3 Technical Report (2025, hyperscale_dense_llm_training)
• Kimi K2: Open Agentic Intelligence (2025, sparse_memory_efficient_scaling)
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models (2025, sparse_memory_efficient_scaling)
• Kimi K2.5: Visual Agentic Intelligence (2026, inference_time_compute_post_training)
• Qwen3.5-Omni Technical Report (2026, generative_media_compute)

后续计算范式下过时或退居次要的内容

仅通过已链接的阅读卡追踪，不将本方法页视为独立证据来源。