perf-host-optimization

Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned). Runs iterative profile-analyze-optimize-validate rounds. Use when GPU utilization is low or optimizing PyExecutor throughput.

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저장소: NVIDIA/TensorRT-LLM
최근 소스 활동: 2026년 10월 30일 17:50
감지된 SKILL.md 언어: 영어
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Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/NVIDIA/TensorRT-LLM/tree/main/.claude/skills/perf-host-optimization.

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npx skills add https://github.com/NVIDIA/TensorRT-LLM --skill perf-host-optimization

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SKILL.md

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name	perf-host-optimization
description	Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned). Runs iterative profile-analyze-optimize-validate rounds. Use when GPU utilization is low or optimizing PyExecutor throughput.

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trtllm-modeling-v2-entry

Load before implementing, modifying or certifying a TensorRT-LLM ModelingV2 catalog entry (OpWrapper) or its GPU test, including verification at new shapes/dtypes/architectures and target-local variants. Not needed for catalog discovery, assembling a target from unchanged entries, running model-level gates, or tuning serving settings.

2026-11-09

trtllm-modeling-v2-features

Load when working on, testing or debugging a TensorRT-LLM engine feature around a ModelingV2 target -- KV-cache block (prefix) reuse, disaggregated serving, chunked prefill, KV-cache dtype (fp8/nvfp4 and their scales), CUDA graphs and the overlap scheduler, multimodal inputs, speculative decoding, or attention-DP/TP/EP where it changes one of those -- including a check that fails on a feature field, or a number that moves only with a feature on. Says which engine stage owns the feature and what the target must honour, where its code and tests are in the tree, and how to prove it from a CPU check up to the flow's accuracy, disagg or acceptance gate. Not needed to write or certify one catalog entry (trtllm-modeling-v2-entry) or to debug core accuracy with every feature at its default.

2026-11-09

trtllm-moe-develop

Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability. Always use for any modification, review, refactor, or design planning that touches MoE modules, including tensorrt_llm/_torch/moe/fused_moe, ConfigurableMoE, MoE implementations (MoEImplBase leaves and families), backend selection (moe_resolution, can_implement, implementation identities), MoEScheduler/moe_scheduler.py, forward execution/chunking, communication strategies, EPLB, quantization/weight handling, activation support, routing, factories, MoE docs, or MoE tests. Also use when the user asks whether a MoE design follows the current architecture or whether a MoE refactor is reasonable.

2026-11-08

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perf-host-optimization

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