llmtest-perf helps you check how an LLM system behaves under load. It focuses on speed, stability, and repeatable test runs. Use it to compare builds, spot slowdowns, and track changes over time.
This project is made for Windows users who want a simple way to run performance checks without setting up a large toolchain by hand.
Use this link to visit the page and download the project:
If you see a ZIP file or release package on the page:
- Click the download link.
- Save the file to your computer.
- Open the downloaded file in File Explorer.
- Extract the files if they are in a ZIP folder.
- Open the project folder.
If the package includes a ready-to-run app or script, double-click it to start. If it includes setup steps, follow the file names in the folder, such as README, run, or install.
This tool works best on a Windows PC with:
- Windows 10 or Windows 11
- At least 8 GB of RAM
- 2 GB of free disk space
- Internet access for model or service checks
- A modern CPU
- Optional GPU support for faster test runs
For smooth use, keep other heavy apps closed while you run tests.
llmtest-perf is built for validation and regression testing. In plain terms, it helps you answer questions like:
- Did the latest update slow things down?
- Does the system still respond the same way?
- Are performance numbers staying within range?
- Did a change affect latency or throughput?
- Do test results match past runs?
It is useful for local checks, CI runs, and repeat testing on the same machine.
- Run performance checks for LLM inference systems
- Compare current results with earlier runs
- Track response time and throughput
- Use repeatable test cases for regression testing
- Fit into CI/CD workflows
- Support Python-based testing with pytest
- Work with machine learning and PyTorch-based setups
- Record results for later review
- Help catch slowdowns before release
Follow these steps after you download the project:
- Open the project folder.
- Look for a file named
README.md,run.bat,start.bat,main.py, or a similar launch file. - If you see a
.batfile, double-click it. - If you see a Python file, open it with Python if your system is already set up for that.
- Wait for the tool to finish its setup checks.
- Start your test run from the screen or command window that opens.
If the project uses a test folder, look for sample test cases or config files and use those first.
If the package needs a small setup, follow this order:
- Install Python 3.10 or newer.
- Open the project folder.
- Find a file named
requirements.txt. - Install the required packages.
- Run the main test command.
- Review the output logs.
A common setup flow on Windows looks like this:
- Open Command Prompt.
- Go to the project folder.
- Run the install command shown in the project files.
- Start the test suite.
- Watch the console for progress and results.
llmtest-perf is useful for checking:
- Latency
- Response time
- Request throughput
- Test stability
- Regression changes
- Resource use
- Repeated run consistency
These checks help you see if a new build performs better, worse, or the same.
Use this tool when you want to:
- Test a new model build before release
- Compare two inference versions
- Check if a code change made responses slower
- Run the same test many times and compare results
- Add performance checks to a build pipeline
- Review test history after each update
A typical project like this may include:
README.mdfor setup and usetests/for test casesconfigs/for run settingsresults/for saved outputscripts/for helper commandsrequirements.txtfor Python packagespyproject.tomlfor project settings
If the folder names differ, use the files that match these roles.
After setup, the usual flow is:
- Open the project folder.
- Start the main run file or command.
- Choose a test profile if one appears.
- Select the model or endpoint you want to check.
- Start the run.
- Wait for the results screen or log file.
When the test finishes, review the timing data and compare it with past runs.
Look for these parts in the output:
- Average response time
- Slowest requests
- Fastest requests
- Total requests tested
- Pass or fail status
- Any error messages
- Comparison against a baseline
If a result changes a lot between runs, test again under the same conditions.
- Run tests on the same machine for fair comparison
- Keep the same test size when you compare results
- Close apps that use a lot of memory
- Use the same model version for repeat checks
- Save results after each run
- Check the config file before changing test values
A simple workflow may look like this:
- Download the project from the link above.
- Open it on your Windows PC.
- Install the required tools if asked.
- Run the test suite.
- Compare the new results with the last run.
- Check for slower response times or missed targets
If the tool does not start:
- Check that Python is installed
- Make sure you opened the right file
- Confirm that all required packages are installed
- Try running from Command Prompt
- Check the console for the first error line
If the results look wrong:
- Run the same test again
- Use the same input data
- Check the model or endpoint address
- Confirm that no other heavy app is running
- Review the config values
This project fits users who want to:
- Validate LLM inference speed
- Track performance changes over time
- Run simple regression checks
- Use a Python-based test setup
- Add performance tests to a development process
If you are new to the project, start with these files:
README.mdrequirements.txtrun.batmain.pytests/configs/
These files usually show how to install, start, and test the app