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How to benchmark AgentJev serving latency on your load with Agent Jev

Run the repository's serving benchmark to see case latency and throughput on your own GPU.

Who it is forAn infrastructure engineer deciding whether an AgentJev server fits the latency budget of a planned agent loop.

The problemThe published timings come from one RTX 5060, so you need numbers for your own hardware and request mix.

The outcomeCase latency at P50 and P90, throughput in cases per second, and backbone token counts for your load.

What you need first

  • Python with PyTorch and Transformers installed (the README benchmarks used PyTorch 2.9.1+cu128 and Transformers 4.57.6)
  • A GPU; the README timings were measured on one RTX 5060 with 8 GB
  • Git access to clone malevrigns/agent-jev and download weights from the linked Hugging Face model repository
  • A state to evaluate, such as a test runner summary, plus questions you have already phrased as boolean, choice, or score

Steps

  1. Clone the repository and fetch the weights you plan to serve.
  2. Start the server with the checkpoint and temperatures you plan to use.
  3. Run python -m jev_service.benchmark_serving with the same checkpoint on the same machine.
  4. Compare P50 case latency, P90 case latency, and throughput against your agent loop's time budget.
  5. Repeat with a wider candidate load if your questions have many options.

Prompt for your agent

In the agent-jev repository at <repo-path>, run `python -m jev_service.benchmark_serving` on this machine and report P50 case latency, P90 case latency, and throughput. Verify that the run used the same checkpoint you plan to deploy.

Caveat. The README's latency table measures one load on one GPU and warns it is not a promise for every prompt, so rerun the benchmark on your own hardware and prompts.

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