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model-inference-optimize

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优化实际模型推理链路，将正确性对齐、分段 profiling、显存与数据搬运、TensorRT/ONNX/PyTorch 后端、attention/kernel、FP8/compile、缓存与少步采样、质量回归、GPU 成本和服务验收串成同一实验闭环。当用户要求推理提速、降低显存或 GPU 成本、复现模型效果、定位 GPU 利用率低、优化图像/视频/扩散模型或自托管 LLM 时使用，提供 model inference performance optimization 的脱敏案例。纯 API 调用、提示词或 token 用量优化，以及电脑卡顿，不进入本技能的模型推理实验流程。

skills/model-inference-optimize/SKILL.md

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# 模型推理优化系统

把明确的推理工作负载优化到可验证的结果。沿用同一任务定义、实验记录和验收标准，按瓶颈选择专项，不把所有优化技术轮流试一遍。

## 入口与资料

| 当前问题 | 从哪里开始 | 按需读取 |
|---|---|---|
| 模型尚未跑通、迁移后效果不对 | 任务 → 正确性对齐 | [专项方法](references/playbook.md) 的「复现与后端迁移」 |
| 延迟高、GPU 利用率低、显存不足 | 任务 → 基线 → profile | 专项方法的「测量」「显存与数据流」 |
| 换 attention/kernel、FP8 或 compile | 先证明热点和实际运行路径 | 专项方法的「算子与编译」 |
| 减少 steps、换蒸馏模型或近似 decoder | 先定义允许的质量变化 | 专项方法的「近似推理」 |
| 复用同类优化经验 | 按当前瓶颈选案例 | [脱敏案例](references/cases.md)，只读相关小节 |
| 已有优化要接入服务或证明真实收益 | 质量回归 → 完整请求 → 服务验收 | 专项方法的「质量评测」「服务与成本」 |
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