https://answer.freetools.me/tags/gqa/ Recent content in GQA on Answer Hugo -- 0.152.2 zh-cn Thu, 12 Mar 2026 07:58:20 +0800 https://answer.freetools.me/%E5%A4%9A%E6%9F%A5%E8%AF%A2%E6%B3%A8%E6%84%8F%E5%8A%9B%E4%B8%BA%E4%BB%80%E4%B9%88%E5%85%B1%E4%BA%AB%E4%B8%80%E4%B8%AAkv%E5%A4%B4%E8%83%BD%E8%AE%A9%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E6%8F%90%E9%80%9F%E6%95%B0%E5%80%8D/ Thu, 12 Mar 2026 07:58:20 +0800 https://answer.freetools.me/%E5%A4%9A%E6%9F%A5%E8%AF%A2%E6%B3%A8%E6%84%8F%E5%8A%9B%E4%B8%BA%E4%BB%80%E4%B9%88%E5%85%B1%E4%BA%AB%E4%B8%80%E4%B8%AAkv%E5%A4%B4%E8%83%BD%E8%AE%A9%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E6%8F%90%E9%80%9F%E6%95%B0%E5%80%8D/ 深入解析多查询注意力(MQA)如何通过共享KV头解决Transformer推理的内存带宽瓶颈。从自回归解码的特点、KV缓存的内存困境、Roofline模型的性能分析，到MQA的核心思想、实际性能数据和质量权衡，系统阐述这项让大模型推理提速数倍的技术。 https://answer.freetools.me/kv-cache%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E7%BC%93%E5%AD%98%E5%86%B3%E5%AE%9A%E4%BA%86%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E7%9A%84%E9%80%9F%E5%BA%A6%E5%92%8C%E6%88%90%E6%9C%AC/ Thu, 12 Mar 2026 00:32:19 +0800 https://answer.freetools.me/kv-cache%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E7%BC%93%E5%AD%98%E5%86%B3%E5%AE%9A%E4%BA%86%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E7%9A%84%E9%80%9F%E5%BA%A6%E5%92%8C%E6%88%90%E6%9C%AC/ 深入解析大模型推理中KV Cache的工作原理、内存消耗计算、PagedAttention优化、GQA架构演进，以及如何在实际部署中进行容量规划。 https://answer.freetools.me/gqa%E4%B8%BA%E4%BD%95%E8%83%BD%E8%AE%A9llama-2%E6%8E%A8%E7%90%86%E9%80%9F%E5%BA%A6%E7%BF%BB%E5%80%8D%E4%BB%8Emha%E5%88%B0mqa%E7%9A%84%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9E%B6%E6%9E%84%E6%BC%94%E8%BF%9B/ Mon, 09 Mar 2026 04:49:02 +0800 https://answer.freetools.me/gqa%E4%B8%BA%E4%BD%95%E8%83%BD%E8%AE%A9llama-2%E6%8E%A8%E7%90%86%E9%80%9F%E5%BA%A6%E7%BF%BB%E5%80%8D%E4%BB%8Emha%E5%88%B0mqa%E7%9A%84%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9E%B6%E6%9E%84%E6%BC%94%E8%BF%9B/ 深入解析大模型注意力机制的核心优化技术。从MHA的KV Cache内存瓶颈，到MQA的极端压缩，再到GQA的平衡方案和DeepSeek的MLA低秩压缩。涵盖Llama 2/3、Mistral等主流模型的GQA配置、KV Cache内存计算公式、以及从320MB到40MB的内存节省实战数据。