Analyze GPU operation timings and execution traces to find costly data access, repeated computation, launch overhead, and synchronization waits. Check whether improving an individual operation actually reduces the time needed to complete a full request.
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诊断 GPU 算子、融合、编译和运行时提交的性能问题。用于判断重读、重算、启动或同步是否位于请求关键路径。
安装前可以查看交给 AI 代理的指令内容。
适用：有算子 trace、kernel 计时或编译方案，想解释局部优化能否缩短完整请求。依据 manuscripts/05-算子与运行时.md 第 5.1—5.6 节。
输出瓶颈证据、预期节省与实际端到端变化。单个 kernel 加速而任务无明显变化时，先检查是否被其他阶段遮蔽，不直接归因于测量错误。
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Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.
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Plan when an agent’s tool environments are created, kept available, released, and restored. Estimate peak concurrency, resource use during work and waiting, total task duration, and cost across model calls, tool execution, validation, and retries.
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Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.
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Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.
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Compare running models or tasks on a device, nearby edge infrastructure, or in the cloud. Break down interaction delays, data transfers, energy use, costs, and failure recovery to assess deployment choices for multimodal and real-time agents.
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Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.
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