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How to benchmark a local model server you already run with Aa Agentperf Local

Point the tool at a running OpenAI-compatible server and get tokens per second and latency numbers.

Who it is forA developer who already runs llama.cpp, LM Studio, vLLM, SGLang, or Ollama locally and wants to know how fast it serves agent-style requests.

The problemChat tests do not show how a server handles long agent conversations where every request carries the full history.

The outcomeA results directory with summary.json holding output tokens per second and median and p95 first-token and turn times.

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. Start your model server and note its base URL and model name, for example http://127.0.0.1:8080/v1 for llama.cpp
  3. Run agentperf-local run --base-url http://127.0.0.1:8080/v1 --model served-model --output-dir results/my-server (inferred, check the README)
  4. Open results/my-server/summary.json and read the headline numbers

Prompt for your agent

Using the agentperf-local repo from ArtificialAnalysis, run `agentperf-local run` against the OpenAI-compatible server at <base-url> serving model <model-name>, writing output to results/<name>. Confirm that summary.json exists and report output tokens per second and median and p95 turn times. Verify the run is not marked reduced before you report it as comparable.

Caveat. The exact output policy sends ignore_eos, and the tool stops if your server does not honor it. Ollama cannot honor it, so Ollama results use the recorded policy and are not directly comparable.

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