https://answer.freetools.me/tags/gpu%E4%BC%98%E5%8C%96/ Recent content in GPU优化 on Answer Hugo -- 0.152.2 zh-cn Thu, 12 Mar 2026 10:07:21 +0800 https://answer.freetools.me/%E9%9A%90%E8%97%8F%E5%B1%82%E7%BB%B4%E5%BA%A6%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E6%95%B0%E5%AD%97%E5%86%B3%E5%AE%9A%E4%BA%86%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%83%BD%E5%8A%9B%E8%BE%B9%E7%95%8C/ Thu, 12 Mar 2026 10:07:21 +0800 https://answer.freetools.me/%E9%9A%90%E8%97%8F%E5%B1%82%E7%BB%B4%E5%BA%A6%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%AA%E6%95%B0%E5%AD%97%E5%86%B3%E5%AE%9A%E4%BA%86%E5%A4%A7%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%83%BD%E5%8A%9B%E8%BE%B9%E7%95%8C/ 深入解析大模型隐藏层维度选择的技术原理：从GPU硬件约束到理论权衡，从参数计算公式到主流模型架构对比，揭示为什么768、4096这些数字成为行业标准。 https://answer.freetools.me/%E4%B8%80%E4%B8%AA%E8%AF%B7%E6%B1%82%E5%85%88%E7%BB%93%E6%9D%9F%E4%B8%BA%E4%BD%95%E6%95%B4%E6%89%B9%E9%83%BD%E8%A6%81%E7%AD%89%E4%BB%8E%E9%9D%99%E6%80%81%E6%89%B9%E5%A4%84%E7%90%86%E5%88%B0%E8%BF%9E%E7%BB%AD%E6%89%B9%E5%A4%84%E7%90%86%E7%9A%84llm%E6%8E%A8%E7%90%86%E9%9D%A9%E5%91%BD/ Mon, 09 Mar 2026 04:21:14 +0800 https://answer.freetools.me/%E4%B8%80%E4%B8%AA%E8%AF%B7%E6%B1%82%E5%85%88%E7%BB%93%E6%9D%9F%E4%B8%BA%E4%BD%95%E6%95%B4%E6%89%B9%E9%83%BD%E8%A6%81%E7%AD%89%E4%BB%8E%E9%9D%99%E6%80%81%E6%89%B9%E5%A4%84%E7%90%86%E5%88%B0%E8%BF%9E%E7%BB%AD%E6%89%B9%E5%A4%84%E7%90%86%E7%9A%84llm%E6%8E%A8%E7%90%86%E9%9D%A9%E5%91%BD/ 深入解析连续批处理（Continuous Batching）如何解决大模型推理中的GPU利用率问题。从ORCA论文的迭代级调度到vLLM的实现，全面阐述这项让推理吞吐量提升数十倍的核心技术。涵盖prefill与decode阶段、KV缓存、选择性批处理、chunked prefill等关键技术。 https://answer.freetools.me/%E4%B8%BA%E4%BB%80%E4%B9%88pagedattention%E8%83%BD%E8%AE%A9%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E5%90%9E%E5%90%90%E9%87%8F%E6%8F%90%E5%8D%87%E6%95%B0%E5%80%8D%E4%BB%8Ekv-cache%E5%86%85%E5%AD%98%E7%A2%8E%E7%89%87%E5%88%B0%E5%88%86%E9%A1%B5%E7%AE%A1%E7%90%86%E7%9A%84%E7%B3%BB%E7%BB%9F%E7%BA%A7%E4%BC%98%E5%8C%96/ Mon, 09 Mar 2026 04:02:20 +0800 https://answer.freetools.me/%E4%B8%BA%E4%BB%80%E4%B9%88pagedattention%E8%83%BD%E8%AE%A9%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E5%90%9E%E5%90%90%E9%87%8F%E6%8F%90%E5%8D%87%E6%95%B0%E5%80%8D%E4%BB%8Ekv-cache%E5%86%85%E5%AD%98%E7%A2%8E%E7%89%87%E5%88%B0%E5%88%86%E9%A1%B5%E7%AE%A1%E7%90%86%E7%9A%84%E7%B3%BB%E7%BB%9F%E7%BA%A7%E4%BC%98%E5%8C%96/ 深度解析PagedAttention如何借鉴操作系统虚拟内存技术解决KV Cache内存碎片问题。从三种内存碎片的根本原因到block table映射机制，全面阐述这项让LLM推理吞吐量提升2-4倍的核心技术。 https://answer.freetools.me/%E5%BD%93%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%88%90%E4%B8%BA%E7%93%B6%E9%A2%88%E4%BB%8Eon%E5%9B%B0%E5%A2%83%E5%88%B0%E7%BA%BF%E6%80%A7%E5%A4%8D%E6%9D%82%E5%BA%A6%E7%9A%84%E6%8A%80%E6%9C%AF%E7%AA%81%E5%9B%B4/ Sun, 08 Mar 2026 13:20:50 +0800 https://answer.freetools.me/%E5%BD%93%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%88%90%E4%B8%BA%E7%93%B6%E9%A2%88%E4%BB%8Eon%E5%9B%B0%E5%A2%83%E5%88%B0%E7%BA%BF%E6%80%A7%E5%A4%8D%E6%9D%82%E5%BA%A6%E7%9A%84%E6%8A%80%E6%9C%AF%E7%AA%81%E5%9B%B4/ 深入解析Transformer注意力机制的计算复杂度瓶颈及其优化方案。从2017年原始Transformer的O(n²)复杂度，到Flash Attention的IO感知优化、Performer的线性注意力、Ring Attention的分布式方案，系统阐述各技术路径的原理、权衡与实际应用。涵盖GPU内存层次结构、稀疏注意力、MQA/GQA等关键优化策略，以及长上下文扩展的技术演进。 https://answer.freetools.me/%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E4%B8%BA%E4%BD%95%E8%BF%99%E4%B9%88%E6%85%A2%E4%BB%8E%E5%86%85%E5%AD%98%E5%B8%A6%E5%AE%BD%E7%93%B6%E9%A2%88%E5%88%B0kv-cache%E4%BC%98%E5%8C%96%E7%9A%84%E6%8A%80%E6%9C%AF%E7%AA%81%E5%9B%B4/ Fri, 06 Mar 2026 12:41:49 +0800 https://answer.freetools.me/%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E4%B8%BA%E4%BD%95%E8%BF%99%E4%B9%88%E6%85%A2%E4%BB%8E%E5%86%85%E5%AD%98%E5%B8%A6%E5%AE%BD%E7%93%B6%E9%A2%88%E5%88%B0kv-cache%E4%BC%98%E5%8C%96%E7%9A%84%E6%8A%80%E6%9C%AF%E7%AA%81%E5%9B%B4/ 深入解析大语言模型推理的性能瓶颈，从内存带宽限制到KV Cache优化的完整技术演进路线。涵盖FlashAttention、PagedAttention、GQA、连续批处理等核心技术，以及vLLM与TensorRT-LLM框架的选型建议。