WebGPU vs CPU: ML Inference Performance in Browsers

Running a neural network in a browser was impractical just a few years ago. Today, WebGPU gives JavaScript access to the device's graphics processor, enabling real-time ML inference that rivals native applications. But how much difference does GPU acceleration actually make for person detection in a security camera context?

What Is WebGPU?

WebGPU is the successor to WebGL. While WebGL was designed for rendering graphics, WebGPU is a modern graphics and compute API that can perform general-purpose GPU computation — exactly what neural network inference requires. It provides access to the GPU's massive parallel processing capabilities through a JavaScript API.

WebGPU is supported in Chrome 113+, Edge 113+, and is under development in Firefox and Safari. On mobile, Android Chrome has full support, while iOS Safari has partial support via the WebKit engine.

Benchmark Results

We tested YOLO26n inference across a range of devices using both WebGPU and CPU (WebAssembly) backends. Results are measured in frames per second (FPS) at the model's native input resolution.

Key observations:

• WebGPU consistently provides 2.7-3.7x speedup across all tested devices
• Flagship phones achieve 18-25 FPS with WebGPU, which is smooth enough for practical person detection
• Budget devices still get usable performance — 8 FPS with WebGPU is functional, while 3 FPS on CPU is borderline
• Desktop GPUs provide the highest throughput, but the gap is smaller because desktop CPUs are also faster

Why GPUs Are Faster for ML

Neural network inference is fundamentally a series of matrix multiplications. A single inference pass through YOLO26n involves millions of multiply-accumulate operations. CPUs process these sequentially (or with limited SIMD parallelism), while GPUs can process thousands of operations simultaneously.

For a 640x640 input image going through YOLO26n, the model performs approximately 8 billion floating-point operations. A modern GPU can execute these in parallel across thousands of cores, completing the work in 25-50 milliseconds. A mobile CPU, limited to 4-8 cores, takes 125-330 milliseconds for the same computation.

Browser Compatibility

WebGPU support varies by browser and platform:

• Chrome/Edge (desktop and Android): Full support since version 113
• Safari (iOS and macOS): Partial support; some features may use a compatibility layer
• Firefox: In development; currently behind a flag
• Samsung Internet: Full support (Chromium-based)

When WebGPU is not available, AiSecam automatically falls back to CPU inference via WebAssembly. The detection still works — it just runs slower. This ensures broad device compatibility while rewarding modern hardware with better performance.

Power and Thermal Implications

Counterintuitively, WebGPU inference often uses less total energy than CPU inference for the same workload. Because the GPU completes the work faster, the processor spends less time in an active state. On a phone, this translates to lower battery drain and less heat generation during extended monitoring sessions.

In our testing, continuous person detection with WebGPU consumed approximately 15-20% less battery per hour than CPU-only inference on a typical Android phone. The GPU gets the work done in one-third the time, and the rest of the system can enter lower-power states more frequently.

Practical Implications for Security Cameras

For a browser-based security camera, the FPS difference between WebGPU and CPU directly affects detection quality. At 20+ FPS, bounding boxes track people smoothly and detection is responsive. At 3-5 FPS, there is noticeable lag between a person's movement and the detection overlay — and some fast movements may be missed entirely between frames.

For home monitoring where people walk (rather than sprint) through the frame, even 5 FPS is adequate. But for scenarios requiring precise tracking or rapid response, WebGPU acceleration makes a meaningful difference.