LocalScore
Mozilla released LocalScore tools, supporting Windows and Linux, helping developers easily conduct LLM model benchmarking.
English
English
If you are trying to run a large language model (LLM) locally and want to know how the model performs on your hardware, Mozilla's just released tool - LocalScore , may be exactly what you need.
LocalScore is a new achievement under the Mozilla Builders program, a benchmarking tool designed for localized LLM systems. It is not only open source, lightweight, but also compatible with mainstream operating systems and supports Windows and Linux.
For developers and AI beginners, this tool greatly lowers the performance evaluation barrier and makes performance testing of local big models no longer complicated.
The core feature of LocalScore is to help users evaluate large language model (LLM) performance in a local environment. It can:
Measure the running speed of the model on the CPU or GPU
Output clear and standardized benchmark results
Supports out of the box, no complicated configuration required
This makes it ideal for individual developers, AI enthusiasts, and researchers who want to test model performance.
| characteristic | User Value |
|---|---|
| Multi-platform compatibility | Supports Windows and Linux to meet the needs of different development environments |
| Flexible deployment method | Can be run as a standalone program or can be called through the Llamafile integrated |
| Localization benchmark support | Testing does not rely on the cloud, improves efficiency and enhances privacy |
| Data storage support | Optionally upload test results to LocalScore .ai platform for comparison and sharing |
| Llama3.1 Model Test Benchmark | Using the official Meta model as the basis, the results are more authoritative and more benchmark |
Using LocalScore is very simple, even if you are a newbie in the AI field, you can get started quickly.
Method 1: Enable via Llamafile
Install the latest version of Llamafile (0.9.2 and above)
Run llamafile --benchmark using the command line to start LocalScore test
Method 2: Use independent binary files
Download the LocalScore executable for your system (Windows or Linux)
Double-click or run through the command line, and follow the prompts to start the performance test
Optional: Upload the result to LocalScore
If you want to share test data with others or view comparison data for different hardware
Test results can be uploaded to LocalScore .ai platform for unified management and viewing
LocalScore is a tool tailored for local LLM users, no matter you are:
AI beginner: Want to see if the locally deployed model runs smoothly
Independent developers: need to compare the model performance of different graphics cards or systems
Researchers: Standardized test results are required for use in papers or reports
Enterprise internal testing team: I hope to conduct hardware compatibility and performance evaluation before deployment
This tool can save you a lot of time and effort.
LocalScore is built based on the open source spirit of the Mozilla Builders program. It relies on the latest architecture of Llamafile 0.9.2, and combines the Llama3.1 model released by Meta as a test reference.
Clear source: Models, test data and tools are all from trusted official sources
Open source verified: users can view the source code and participate in contributions, transparent and controllable
Data privatization: Complete tests locally without uploading sensitive content
This design based on user trust and technical depth makes LocalScore not only "usable", but "professional and trustworthy".
With the increasing number of AI tools, it is becoming increasingly important to understand the performance of models in local hardware.
LocalScore , launched by Mozilla, is such a practical tool to help you speak with data:
No cloud-dependent, privacy-friendly
Easy to get started, professional results
Completely free, open source and transparent
Whether you are a beginner in AI or a developer who is doing model performance optimization, LocalScore is worth your own try.
Go to experience:
LocalScore project page: https://LocalScore.ai
Learn about Mozilla Builders: https://www.mozilla.org/en-US/builders/
