TOOL RANKS LOCAL LLMS BY YOUR HARDWARE

The tool addresses a common problem for developers running LLMs locally: choosing which model works best for their setup. Rather than relying on subjective comparisons, it uses standardized benchmarks to rank performance across different hardware configurations. Users input their hardware specifications—CPU, GPU, RAM, and storage—and receive ranked recommendations based on actual test results. This approach cuts through marketing claims and generic advice to show real-world performance metrics. The project has gained traction on Hacker News with 198 points and 26 comments. The open-source nature allows community contributions to expand supported models and hardware configurations. For those running inference locally, the tool eliminates guesswork about which models will perform adequately on their machines. It's particularly useful for developers evaluating whether to deploy models on edge devices or local servers rather than using cloud APIs.