Most ML engineers deploy their fine-tuned BERT or GPT model and call it done. The result? Inference costs that eat your margin and latency that kills user experience.

The numbers don't lie:

• A standard BERT-base runs ~12ms per inference on a V100

• After proper optimization (pruning + quantization + ONNX), you can hit <2ms on the same hardware

• That's the difference between a $15k/month GPU bill and a $2k one

The optimization stack most teams ignore:

1. Pruning — Remove 40-60% of attention heads and FFN weights with <1% accuracy loss using movement pruning. Most heads are redundant for downstream tasks.

2. Quantization — INT8 quantization alone gives 2-4x speedup. Combine with INT4 for weights and you're looking at 8x memory reduction.

3. Knowledge Distillation — DistilBERT showed the way, but task-specific distillation crushes it. A 6-layer student can match a 12-layer teacher on your specific task.

4. Runtime optimization — ONNX Runtime and TensorRT apply graph-level optimizations (operator fusion, memory planning) that PyTorch can't do eagerly.

The compounding effect

These techniques multiply. Pruning + quantization + distillation + runtime optimization together can deliver 10-50x end-to-end speedup.

We built a full course covering every technique with hands-on code and a capstone project where you optimize a real NLP pipeline from scratch.

If you're spending more than you should on inference — or your model is too slow for real-time — this is the fix.