model-optimize

To optimize models for NPU inference, you orchestrate a single-card workflow applying staged KVCache staticization, fusion operator optimization, and graph-mode adaptation. This enforces stepwise validation after each stage to ensure traceable performance deltas.

Graph-mode adaptation applies only to the Decode stage, while the Prefill stage remains in eager mode. This selective application ensures focused optimization without disrupting the initial processing phase of the inference workflow.

The best way to validate model optimization on Atlas A2 environments is by enforcing stepwise validation after each stage of KVCache, fusion, and graph-mode adaptation. This produces precise implementation traces and performance deltas for auditable records.

Yes, you can coordinate with subagents to ensure traceable progress and quality during single-card model optimization. They help orchestrate end-to-end tasks and compile precise design decisions and performance deltas into a progress document.

To start Stage 0 for end-to-end model optimization, analyze the target model and create a baseline optimization plan. This initial assessment establishes the foundation for subsequent KVCache, fusion, and graph-mode adaptations.

Staged model optimization solves the problem of uncoordinated inference improvements by focusing on fusion operator optimization and non-Attention modules. It ensures ordered execution and stepwise validation to produce auditable performance reports.