<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>MoE on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/moe/</link><description>Recent content in MoE on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 16 Jul 2026 11:19:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/moe/index.xml" rel="self" type="application/rss+xml"/><item><title>MoE架构详解：稀疏激活如何改变大模型经济学</title><link>https://guijiagi.com/posts/b1-cabd68a1/</link><pubDate>Thu, 16 Jul 2026 11:19:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-cabd68a1/</guid><description>深入解析Mixture of Experts架构的技术原理、训练方法和推理优化策略</description></item><item><title>MoE架构深度解析：混合专家模型的训练与推理优化</title><link>https://guijiagi.com/posts/b2-4eded1bd/</link><pubDate>Thu, 16 Jul 2026 10:15:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-4eded1bd/</guid><description>从数学原理到工程实现，全面剖析MoE模型的路由机制、训练挑战与推理优化策略</description></item><item><title>从GPT到Transformer：架构创新的时间线</title><link>https://guijiagi.com/posts/article-89/</link><pubDate>Mon, 13 Jul 2026 07:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-89/</guid><description>回顾从Transformer诞生到2026年的架构演进，梳理每一次关键突破的技术脉络</description></item><item><title>深度解析MoE架构：混合专家模型如何工作</title><link>https://guijiagi.com/posts/article-33/</link><pubDate>Sun, 12 Jul 2026 22:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-33/</guid><description>从路由机制到负载均衡，全面拆解Mixture of Experts架构的设计原理</description></item><item><title>深度解析MoE架构：混合专家模型如何工作</title><link>https://guijiagi.com/posts/b2-0e8204a0/</link><pubDate>Sun, 12 Jul 2026 22:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-0e8204a0/</guid><description>从路由机制到负载均衡，全面拆解Mixture of Experts架构的设计原理</description></item><item><title>MoE混合专家架构2026详解</title><link>https://guijiagi.com/posts/moe-architecture-2026-detail/</link><pubDate>Thu, 02 Jul 2026 10:52:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-2026-detail/</guid><description>从GShard到DeepSeek-V3，全面解析MoE架构的设计演进与工程实践</description></item><item><title>DeepSeek V4发布：训练成本仅GPT-6的1/10</title><link>https://guijiagi.com/posts/deepseek-v4-release/</link><pubDate>Thu, 02 Jul 2026 10:21:00 +0800</pubDate><guid>https://guijiagi.com/posts/deepseek-v4-release/</guid><description>DeepSeek V4发布，以极低成本实现接近GPT-6的性能，再次震撼AI圈</description></item><item><title>Llama 4开源发布：405B参数MoE架构</title><link>https://guijiagi.com/posts/meta-llama4-release/</link><pubDate>Thu, 02 Jul 2026 10:04:00 +0800</pubDate><guid>https://guijiagi.com/posts/meta-llama4-release/</guid><description>Meta发布Llama 4开源大模型，405B参数MoE架构刷新开源模型性能记录</description></item><item><title>MoE架构深度对比：DeepSeek V4 vs Qwen3.5 vs Llama 4</title><link>https://guijiagi.com/posts/moe-architecture-comparison/</link><pubDate>Tue, 30 Jun 2026 11:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-comparison/</guid><description>深度对比三大MoE架构大模型的技术细节和性能表现</description></item><item><title>Llama 4系列评测：Meta开源旗舰的表现</title><link>https://guijiagi.com/posts/llama-4-series-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/llama-4-series-evaluation/</guid><description>全面评测Llama 4系列模型，从405B到8B的开源旗舰表现分析</description></item><item><title>DeepSeek V4完整评测：国产大模型的崛起</title><link>https://guijiagi.com/posts/deepseek-v4-full-evaluation/</link><pubDate>Tue, 30 Jun 2026 10:30:00 +0800</pubDate><guid>https://guijiagi.com/posts/deepseek-v4-full-evaluation/</guid><description>全面评测DeepSeek V4在推理、代码、中文理解等维度的表现，分析国产大模型的突破</description></item><item><title>MoE混合专家模型深度解析：路由机制与负载均衡</title><link>https://guijiagi.com/posts/moe-expert-routing-load-balance/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-expert-routing-load-balance/</guid><description>深入解析MoE混合专家模型的路由机制、负载均衡策略以及在2026年的最新进展</description></item><item><title>MoE混合专家模型深度解析：路由机制与负载均衡</title><link>https://guijiagi.com/posts/moe-mixture-of-experts-routing-load-balancing/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-mixture-of-experts-routing-load-balancing/</guid><description>深入解析MoE混合专家模型的路由机制、负载均衡策略和训练技巧，包含数学推导和工程实现</description></item><item><title>Transformer架构2026：从注意力机制到混合专家的演进</title><link>https://guijiagi.com/posts/transformer-architecture-2026-attention-to-moe-evolution/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-2026-attention-to-moe-evolution/</guid><description>深入解析Transformer架构在2026年的最新演进，涵盖注意力机制优化、MoE混合专家、线性注意力等前沿方向</description></item><item><title>Transformer架构2026：从注意力机制到混合专家的演进</title><link>https://guijiagi.com/posts/transformer-architecture-2026/</link><pubDate>Tue, 30 Jun 2026 09:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-2026/</guid><description>深入解析Transformer架构在2026年的最新演进，涵盖注意力机制优化、混合专家模型、线性注意力等前沿方向</description></item><item><title>MoE 混合专家架构：从 Mixtral 到 DeepSeek V4 的演进</title><link>https://guijiagi.com/posts/moe-architecture-evolution/</link><pubDate>Sun, 28 Jun 2026 11:06:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-evolution/</guid><description>系统梳理MoE混合专家架构从Mixtral到DeepSeek V4的演进历程，解析路由机制、负载均衡与工程优化</description></item><item><title>Transformer 架构 2026 最新演进：从 Attention 到 MoE 再到 Mamba</title><link>https://guijiagi.com/posts/transformer-architecture-2026-evolution/</link><pubDate>Sun, 28 Jun 2026 11:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-2026-evolution/</guid><description>深入解析Transformer架构在2026年的最新演进，涵盖Attention机制优化、MoE混合专家架构、Mamba状态空间模型三大方向</description></item><item><title>MoE 架构深度对比：DeepSeek V4 vs Qwen3.5 vs Llama 4 Behemoth</title><link>https://guijiagi.com/posts/moe-architecture-deepseek-v4-vs-qwen35-vs-llama4-behemoth/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-deepseek-v4-vs-qwen35-vs-llama4-behemoth/</guid><description>三大MoE架构大模型的技术深度对比：路由机制、专家设计、推理效率全方位解析</description></item><item><title>混合专家模型MoE架构剖析</title><link>https://guijiagi.com/posts/moe-architecture-analysis/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-analysis/</guid><description>混合专家模型MoE架构剖析</description></item><item><title>MoE 混合专家架构深度解析：从稀疏激活到专家路由</title><link>https://guijiagi.com/posts/moe-architecture-deep/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/moe-architecture-deep/</guid><description>深入剖析 Mixture of Experts 架构的原理、路由机制、训练策略及工程实现，涵盖 GShard、Switch Transformer、Mixtral 等代表性工作。</description></item><item><title>MoE 内部机制：专家路由、负载均衡与容量因子</title><link>https://guijiagi.com/posts/mixture-of-experts-internals/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mixture-of-experts-internals/</guid><description>深入解析 Mixture-of-Experts 架构的路由机制、负载均衡策略与工程优化</description></item><item><title>混合专家模型深入剖析：从 GShard 到 DeepSeek V4</title><link>https://guijiagi.com/posts/mixture-of-experts-deep-dive/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/mixture-of-experts-deep-dive/</guid><description>全面解析 MoE 架构原理、路由算法、负载均衡及演进历程</description></item><item><title>DeepSeek 技术解析：开源大模型的性价比之王</title><link>https://guijiagi.com/posts/deepseek-technical-analysis/</link><pubDate>Wed, 24 Jun 2026 11:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/deepseek-technical-analysis/</guid><description>DeepSeek 系列模型的技术架构、训练方法和工程优化深度分析</description></item><item><title>DeepSeek V4 完整版发布：开源模型的新巅峰</title><link>https://guijiagi.com/posts/deepseek-v4-%E5%AE%8C%E6%95%B4%E7%89%88%E5%8F%91%E5%B8%83-%E5%BC%80%E6%BA%90%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%96%B0%E5%B7%85%E5%B3%B0/</link><pubDate>Wed, 24 Jun 2026 00:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/deepseek-v4-%E5%AE%8C%E6%95%B4%E7%89%88%E5%8F%91%E5%B8%83-%E5%BC%80%E6%BA%90%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%96%B0%E5%B7%85%E5%B3%B0/</guid><description>DeepSeek V4 完整版正式发布，MoE 架构+1M 上下文+多头潜在注意力，深度解析技术细节与实测表现</description></item><item><title>MoE 混合专家模型选型指南：从 Mixtral 到 DeepSeek</title><link>https://guijiagi.com/posts/mixture-of-experts-guide/</link><pubDate>Tue, 23 Jun 2026 16:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/mixture-of-experts-guide/</guid><description>MoE架构原理、主流模型对比和生产环境选型建议</description></item></channel></rss>