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- Claude Code
- Codex
- Cursor
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Use this resource to measure LLM inference, analyze GPU kernels and compare their variants
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In a terminal, from the folder you want:
npx --yes palatekit@latest install llm-inference-benchmark-kernel-evalWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent runs inference benchmarks (throughput, latency, TTFT, TPOT), captures PyTorch traces and analyzes them with TraceLens to find bottleneck kernels. It compares HIP, CUDA or Triton variants, then drives an optimization loop through end-to-end validation.
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Updated November 2, 2026
You say
“Compare these two vector_add kernel variants and rank them by performance.”

Skill published by amd under the MIT license, delivered in its original version.
Listed unmodified
« magpie-kernel-evaluator » (amd, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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