core practice · coding problem
WASM linear memory growth
Page limit ~2–4GB WASM memory. Model weights 1.5B params float16 ≈ 3GB. Can you load weights + activations in one page tab without sharding?
The mechanism
You can run ML in the browser using libraries like ONNX Runtime Web, TensorFlow.js, or Transformers.js. You trade server cost and user privacy for a slower first load and a limit on model size. WebGPU is fastest, WASM works everywhere, CPU is the fallback. Scope: this card owns the browser as a platform — Workers, WASM, OPFS, model loading and the first-load budget. The GPU backend specifically is `ml-webgpu`; native and mobile execution is `local-on-device-inference`.
Expected evidence
No headroom — need quantization, offloading, or worker sharding.
Try this drill in the practice workspace →
Hints when you need them
- 2 bytes/param for fp16.
- Activations and KV cache add on top.