What problem does it solve? Inference services often stay slow even after DiT and VAE optimization because non-DiT host overhead—mp4/image encoding, worker-to-API transfer, disk writes, weight loading, and first-run compilation warmup—sits on the critical path and is invisible to device-side profilers. This Skill turns that fixed overhead into measurable, verifiable optimizations. ## Core Features & Use Cases - Fixed Overhead Accounting: Breaks host overhead into measurable rows (encode / transfer / disk write / load / warmup) with timing points, byte counts, and share of same-caliber end-to-end latency, since profilers only see device kernels. - Delivery & Transfer Optimization: Diagnoses encoding parameters, hop-by-hop transfer copies, redundant disk writes, and safe async offloading that preserves return semantics. - Loading & Warmup Optimization: Optimizes weight loading paths and preheats existing compile/graph caches with production-matching shapes, verified by cache-hit criteria rather than total time. - Use Case: Your video generation service is still slow after DiT and VAE tuning, and profiling shows almost nothing. Use this Skill to build a fixed-overhead ledger, find that mp4 encoding and result transfer dominate delivery time, and verify improvements with same-window A/B measurement. ## Quick Start Ask the AI to analyze why mp4 encoding and result transfer are consuming a large share of end-to-end latency in your inference service and produce a fixed-overhead account with optimization steps.