inference optimization

Every AI system spends quality to buy speed, whether or not the team admits it. What scores is knowing the exchange rate for each knob, taking the free wins first, and having already decided what you will sell when the system saturates.

A PyTorch model in eager mode is not the fastest form for inference. The signal is knowing that ONNX buys portability while TensorRT-style compilers buy hardware-tuned speed, and precisely which optimizations get you there. Here is the answer.

Sampling cost factors cleanly into steps, forward passes per step, and cost per step, and each factor has a different lever. Two of the biggest wins are free, and most teams take neither.