Efficient Transformer Deployment — Quantisation & Serving

I will optimise your transformer model for production deployment — applying knowledge distillation to a smaller student transformer, dynamic or static INT8 quantisation, ONNX export for cross-platform serving, and TensorRT optimisation for GPU inference — delivering a model that is significantly faster and smaller than the original with minimal performance degradation. Production transformer deployment at scale faces a compute cost challenge that research model configurations don't address — a 340M parameter BERT model serving 1000 requests per second requires GPU infrastructure that most businesses cannot justify; distillation to a 66M parameter DistilBERT and INT8 quantisation typically achieves 4-6x speedup with less than 2% accuracy loss.

The optimisation covers distillation training from your teacher transformer, quantisation calibration, ONNX export, TensorRT plan compilation for GPU inference, latency benchmarking at various batch sizes, and an accuracy comparison across optimisation levels.