Model Compression & Optimization

Quantization-aware training (QAT) integrates quantization directly into the training process, simulating low-precision arithmetic (e.g., 8-bit or 4-bit) so the model learns to be robust to quantization noise and maintains accuracy despite reduced bit-widths.

This contrasts with post-training quantization, which often causes sharp accuracy drops; QAT is essential when deploying to memory-constrained hardware like Jetson Nano, Google Coral, or microcontrollers that cannot afford significant accuracy loss.

The result is a production-ready model that is 2-8x smaller and 2-5x faster while preserving the accuracy needed for safety-critical or inspection-sensitive workloads.