You've been told massive Transformer models like BERT are simply too large for client-side devices. They are wrong.
In a new study, I deployed a state-of-the-art misinformation detector that runs completely offline, on standard CPU hardware, and fits easily into a browser extension. The results are mind-blowing:
Size Killed: I slashed the model's footprint from a massive 255.45 MB down to a tiny 64.45 MB (a whopping 74.8% size reduction!). This is critical—it easily clears the 100 MB threshold for browser extension deployment.
Speed Doubled: Inference latency was reduced by 55.2% (from 52.73 ms to a real-time 23.58 ms), establishing feasibility for synchronous user interaction.
The key to achieving this isn't just DistilBERT. It’s the two-step compression pipeline: Dynamic Quantization (INT8) and ONNX Runtime Optimization. Ready to put the power of a transformer directly into the user's hands?
Top comments (1)
This was super fascinating, I’m really new to ML, so the idea of something as big as BERT running offline is totally new to me. I don’t know much about quantization or ONNX yet, but your post made me curious about how these optimizations shrink a model without breaking it.
If you ever share a beginner-friendly breakdown, I’d love to read it. I’m trying to learn this from the basics and would appreciate any pointers on where to start.