Benchmark Your
Neural Processing Unit
Professional-grade performance analysis using real TensorFlow matrix operations. Measure actual Browser FLOPS, WebGL Efficiency, and Inference Latency.
Test runs multiple iterations. Please do not switch tabs.
Comprehensive NPU Telemetry
Deep dive into your hardware's AI acceleration capabilities with industrial-grade metrics.
Real-time GFLOPS
Quantify Billions of Floating-point Operations Per Second to gauge raw AI throughput in the browser.
WebGL/WebGPU
Directly access hardware acceleration via TensorFlow.js backends without installing native drivers.
Precision Analytics
Perform tests using FP32 (Single Precision) tensors to simulate heavy deep learning training loads.
Understanding Neural Processing Units (NPUs)
In the rapidly evolving landscape of artificial intelligence, the Neural Processing Unit (NPU) has emerged as a critical component in modern computing architectures. Unlike traditional Central Processing Units (CPUs) or Graphics Processing Units (GPUs), NPUs are specialized hardware designed specifically to accelerate machine learning algorithms and neural network operations.
NPUTest.io provides a comprehensive platform to benchmark and analyze these powerful processors using TensorFlow.js. While standard browsers currently access AI acceleration primarily through the GPU (via WebGL/WebGPU), modern chip architectures (like Apple Silicon and Intel Core Ultra) often unify these resources. This tool measures the system's total effective AI throughput, giving you the most accurate representation of web-based model performance.
Why Benchmark?
Benchmarking is essential to understand the real-world capabilities of your hardware. While manufacturers often advertise peak theoretical TOPS, sustained performance in a browser environment can vary significantly due to WebGL/WebGPU bridge overhead, thermal throttling, and memory bandwidth constraints.
- Verify Claims: Confirm if your device meets the advertised AI performance specs.
- Optimize Models: Determine your browser's capability to run complex LLMs locally.
- Compare Silicon: Direct comparisons between Apple Silicon, Qualcomm Snapdragon, and Google Tensor TPUs.
Testing Methodology
Our testing suite focuses on Matrix Multiplication (MatMul), the fundamental mathematical operation behind all modern neural networks. We allocate large tensors (multi-dimensional arrays) and perform heavy computation.
We use the WebGL backend for widest compatibility and WebGPU where available. On Apple Silicon and modern mobile chips, these backends often leverage the same unified memory architecture as the NPU.