Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.
估算训练或 RL 作业的显存、步时、checkpoint、故障重做和完成期限。用于比较分片、重计算、卸载与集群规模。
Review the instructions the agent will receive before you install.
适用：需要证明某训练目标能否在给定设备和期限内完成。依据 manuscripts/10-训练系统.md 第 10.1—10.6 节。
输出显存峰值、步时分解、有效样本吞吐、恢复开销和期限余量。硬件可行与训练质量可达应分别说明。
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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.
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.
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.
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.
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.
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.