Benchmark Your Hardware for AI
Benchmark and rank your hardware based on real AI performance
How It Works
Run real AI models directly on your hardware, measure performance, and submit verified benchmark results all from your browser.
1. Select & Load a Model
Choose an AI model from the selector and load it. The model is fetched directly from Hugging Face and prepared locally in your browser.
2. Run the Benchmark
Click Run to execute real AI inference on your hardware using WebGPU. No cloud execution or server side compute is used.
3. Measure Performance
We measure load time, latency, throughput, and token generation speed to calculate normalized hardware performance scores.
4. Submit Results
Once complete, you can submit your benchmark results. Hardware details and performance metrics are stored to build a public hardware ranking database.
Why It Matters for Hardware Buyers
Choosing hardware for AI is confusing. Marketing specs don’t reflect real performance — this benchmark shows what actually matters.
Real AI Performance, Not Specs
Clock speeds, core counts, and “AI TOPS” don’t tell you how fast models actually run. These benchmarks measure real AI inference on real consumer hardware.
Compare Before You Buy
See how laptops, desktops, GPUs, and integrated graphics perform across the same workloads. No vendor bias, no synthetic-only rankings.
Avoid Overpaying
Many systems cost more without delivering better AI performance. Real benchmarks help identify the best performance per price options.
Know What Runs Locally
Not all devices can run AI models smoothly. Benchmarks reveal which hardware can load, run, and sustain local AI workloads reliably.
Future-Proof Your Purchase
As local AI adoption grows, knowing real performance today helps you choose hardware that stays relevant longer.
Community-Verified Results
Results are generated by real users on real devices. The more people benchmark, the clearer the picture becomes for future buyers.
Join Our Community
This community is for people building and experimenting with edge AI — running models locally, optimizing performance, and pushing AI beyond the cloud.
Work together on projects involving WebGPU, WebLLM, on-device inference, browser-based AI, NPUs, and real-world hardware benchmarking. Share experiments, failures, and wins.
- Edge and on-device AI applications
- WebGPU and browser-based ML experiments
- Optimizing tokens-per-second on real hardware
- Local LLMs, vision models, and multimodal workloads
- Comparing GPUs, iGPUs, and NPUs for AI inference
No sign-up required to benchmark. Join the Discord to collaborate, propose edge-AI projects, and help shape future experiments.