WebGPU for ML Engineers: From CUDA to the Browser

Why WebGPU?

CUDA dominates GPU computing, but it locks you into NVIDIA hardware and server-side deployments. WebGPU brings GPU-accelerated computing to the browser, enabling inference on any device with a modern GPU — no drivers, no installation, no server costs.

The Migration Path

Moving from CUDA to WebGPU requires rethinking your compute shaders. Here’s the key mapping:

• CUDA kernels → WGSL compute shaders
• cudaMalloc → GPUBuffer creation
• cudaMemcpy → writeBuffer / mapAsync
• Block/thread hierarchy → workgroup/invocation model

Matrix Multiplication Example

The bread and butter of neural networks is matrix multiplication. In WebGPU, a tiled matmul shader looks remarkably similar to its CUDA counterpart, but uses WGSL syntax and workgroup shared memory instead of __shared__.

Performance Results

On an M2 MacBook Pro, our WebGPU transformer achieves 85% of the throughput of a native Metal implementation. On discrete GPUs (RTX 4090), WebGPU reaches 70% of CUDA performance — surprisingly competitive for a browser-based solution.

Gotchas and Limitations

• No dynamic parallelism: WebGPU doesn’t support launching compute shaders from within other shaders
• Buffer size limits: Some implementations cap buffer sizes at 256MB
• Precision: f16 support is still an extension, not universally available
• Debugging: GPU debugging tools are still maturing compared to CUDA’s ecosystem