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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为给定模型负载比较加速器的容量、有效计算、内存带宽、功耗与成本。用于硬件选型或解释标称峰值与实测差异。
安装前可以查看交给 AI 代理的指令内容。
适用：模型与负载已明确，需要判断哪类设备能在期限和预算内完成任务。依据 manuscripts/04-加速器架构.md 第 4.1—4.8 节。
给出“可行性 → 性能下界 → 实测 → 能耗/成本”的比较表，并指出决定选择的约束。没有同口径的实测时，结论标为预测；单卡选型之外的通信代价转用 parallelism-planning。
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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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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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