Quantizing ML Models from 32-bit to 8-bit for Edge Deployment

Why Quantization Matters

At Zeroeka (IIT Madras incubated), our ML models needed to run on resource-constrained embedded devices. Full 32-bit float models were simply too large and slow. Quantization to 8-bit integers was the answer.

Post-Training Quantization with PyTorch

Using PyTorch's built-in quantization APIs, we reduced model size by ~4x with minimal accuracy loss. The key was using representative calibration datasets to minimize quantization error.

Iterative Evaluation

We evaluated every quantized model with precision, recall, and F1-score against the full-precision baseline — iterating until we hit acceptable thresholds for production deployment.