Stop Shipping 50 MB Models to Mobile Browsers
You finally got the model running locally. npm run build passes. You deploy.
Then the alerts start: “Page load is 12 seconds on Android.” “WebGPU crashed, fell back to CPU, now it’s frozen.” “Users are running a model from three releases ago because the Service Worker cached the weights.”
Client-side inference isn’t “just frontend work” anymore. It’s a distinct engineering discipline where bundle budgets, GPU driver quirks, and cache invalidation strategies decide if your feature ships or gets reverted. Most teams learn these lessons the hard way—in production, during an incident.
What’s Inside the Checklist
- Quantization Decision Matrix: Stop guessing INT4 vs. INT8 vs. FP16. Get accuracy/latency trade-offs mapped per model class (LLM, Whisper, YOLO, embeddings) so you pick the smallest artifact that still hits your quality bar.
- WebGPU Fallback & Validation Strategy: A deterministic probe sequence: detect → benchmark → fallback → telemetry. No more “works on my MacBook” surprises on Snapdragon or integrated Intel.
-
Cache-Busting & Versioning Protocol: Immutable weight filenames,
Cache-Controlheaders that actually work, and a CI gate that blocks merges if the runtime hash drifts. -
CI/CD Gates for Runtime Health: Scripts to enforce bundle size budgets, WASM SIMD support checks, and memory ceiling tests before you hit
main.
Who This Is For
- Frontend Engineers owning the first ML feature in the repo.
- ML Engineers tired of “it works in Python” handoffs.
- Tech Leads needing a shared vocabulary to review client-side AI PRs confidently.
Try This Tonight
Open your current build. Run webpack-bundle-analyzer (or vite-bundle-visualizer). Filter for .bin, .onnx, or .safetensors. If the top three chunks exceed 2 MB gzipped combined, you have a quantization gap. The checklist gives you the exact matrix to close it in one sprint.
Ship Confidently Tomorrow
Grab the Client-Side AI Deployment Checklist: Ship Fast, Run Lean for EUR 16.99. One PDF. Zero fluff. Immediate applicability.
👉 https://coldriver8.gumroad.com/l/ffuyac
AI-assisted content - reviewed by author.
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