Estimate how model architecture, precision, and context length affect weight storage, generation state, computation, and memory traffic. Compare models and attention designs with separate resource estimates for input processing and token generation.
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比较模型配置或架构改变对权重容量、KV 状态、前向计算和访存的影响。用于选模型、上下文或注意力与 MoE 方案。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
适用：讨论长上下文、GQA/MLA、MoE、精度或 batch 变化时，先量化模型本身的资源需求。依据 manuscripts/02-模型架构.md 第 2.1—2.6 节。
输出按 prefill、单步 decode、完整请求分列的容量、FLOPs、读写字节表。模型配置与示例计算见 calculations/README.md。系统级延迟、排队和调度交给 workload-modeling 或 inference-serving。
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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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。