LLM GPU Benchmark

Speed test your GPU for AI — measure real memory bandwidth and compute with WebGPU, run an actual LLM in your browser to measure true tokens/sec, and see predicted speeds for every popular model on your hardware.

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How This Tool Works

This benchmark provides a comprehensive, real-world assessment of your GPU's performance for AI workloads. Unlike simple theoretical tests, we measure three critical metrics simultaneously:

  • WebGPU Compute: We utilize the modern WebGPU API to stress-test your GPU's raw compute power, measuring memory bandwidth crucial for large models.
  • Live LLM Inference: The core test involves running an actual Large Language Model (LLM) directly in your browser. This measures true tokens/second throughput under realistic conditions.
  • Model Prediction: Based on the live data, the tool calculates and displays predicted performance metrics for popular models (e.g., Llama 3, Mistral), giving you an educated estimate of what hardware is needed for your use case.

By combining these methods, we ensure a holistic view beyond simple FPS counters.

Why This Matters

Understanding your GPU's true AI capabilities is vital for deciding between local deployment and cloud services. A high tokens/sec score means you can run complex, resource-intensive models like 7B parameter LLMs locally without constant internet dependency.

  • Cost Savings: Knowing your actual throughput prevents over-provisioning on cloud services, saving hundreds of dollars per month.
  • Latency Prediction: This test measures latency (time to first token), which is critical for interactive chat applications. A faster score means a more natural user experience.
  • Model Selection: It helps you determine if your current hardware can effectively run models quantized down to 4-bit precision, making powerful AI accessible on consumer machines.

Don't guess; measure the throughput that matters for real-time AI interaction.

Common Mistakes to Avoid

When benchmarking, remember that external factors can skew results. The most common mistake is running other intensive applications in the background.

  • Close Everything: Ensure all non-essential programs (streaming, games, browsers with many tabs) are completely closed to dedicate 100% of GPU resources to the test.
  • Thermal Throttling: If your machine is hot, performance will drop drastically. Use a stable power source and ensure proper ventilation before starting the benchmark run.
  • Interpreting Single Scores: Never rely on one number (e.g., just memory bandwidth). Always look at the combination of WebGPU compute score AND the tokens/sec rate for an accurate picture.

A clean, isolated environment is key to repeatable and trustworthy results.

Tips for Best Results

To maximize the accuracy of your benchmark run, preparation is everything. Treat this test like a professional stress analysis.

  • Use Consistent Prompts: When running the live LLM test, use similar length prompts (e.g., 3-sentence questions) across multiple runs to ensure consistency in input processing time.
  • Monitor Temperatures: Keep an eye on your GPU's reported temperature during the run; if it exceeds 85°C, performance may be artificially limited by cooling mechanisms.
  • Run Multiple Times: Instead of taking one reading, perform the entire benchmark sequence (WebGPU + LLM) at least three times and take the average score to mitigate transient hardware fluctuations.

Optimal results require optimal conditions!

Frequently Asked Questions

Common questions about the LLM GPU Benchmark

WebGPU is a modern web standard that allows JavaScript to access the GPU directly, bypassing older APIs like WebGL. We use it for accurate memory bandwidth and compute measurements, providing a real-world assessment of your hardware's raw AI processing power.
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