SemiAnalysis 详细介绍了优化AI推理成本的三种方法，重点在于最大化硬件利用率。这些方法包括按阶段（预填充和解码）划分工作负载，按层（注意力层和前馈网络）划分，以及按时间（交错执行窗口）划分。这些策略的核心原则是识别并填补空闲的计算资源，最终降低每token的成本，并有望推动AI服务需求的增长。 AI
影响 这些优化策略旨在显著降低AI推理的成本，可能导致更广泛的应用和新出现的应用场景。
排序理由 对第三方来源的AI推理优化技术的分析。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
A quick map of the three cuts. 🟠 Phase. Every request does two jobs. Prefill reads your prompt; decode writes the answer one token at a time. The two stress hardware differently, so each gets its own chips instead of sharing. 🟠 Layer. Attention lets tokens share context, which
Inference keeps getting carved up, and every cut makes intelligence cheaper. First we split by phase: prefill on one set of chips, decode on another. Then by layer: attention on HBM-rich GPUs, the feed-forward network on SRAM-based silicon. Now by time itself: workloads sliced h…