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How to download and benchmark a pinned catalog model with Aa Agentperf Local

Let the tool fetch a verified model, start llama.cpp, run the replay, and stop the server.

Who it is forA local AI enthusiast or hardware reviewer who wants a reproducible benchmark of a specific model on their own machine.

The problemSetting up a model server, pinning the exact model files, and running a comparable replay by hand is error-prone.

The outcomeA results directory with the replay summary, plus the server log and deployment record written by the managed run.

What you need first

  • uv installed (it fetches Python 3.12 if needed)
  • A model server you already run with an OpenAI-compatible API, or a framework the tool can start (llama.cpp, vLLM, SGLang, or Splash on Apple Silicon)
  • For full results, a server or device that can run a 65,536-token context at batch size 1
  • Docker only if you want live tool calling

Steps

  1. Install the tool with uv tool install agentperf-local
  2. Make sure llama-server is on your PATH
  3. Run agentperf-local managed-run --profile-id qwen38-27b-q4-k-m --framework llama-cpp --output-dir results/qwen38-27b (inferred, check the README)
  4. Check summary.json and the server log in results/qwen38-27b

Prompt for your agent

Using the agentperf-local repo from ArtificialAnalysis, run `agentperf-local managed-run` with --profile-id qwen38-27b-q4-k-m and --framework llama-cpp, writing to results/<name>. Expect a summary.json and a server log in that directory. Verify that every model file passed its SHA-256 check and that the run is not marked reduced.

Caveat. A full comparable run needs a 65,536-token context at batch size 1, so a device that cannot fit it can only produce a reduced run that is not comparable with full results.

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