How can I optimize inference latency in my AI models?

How can I optimize inference latency in my AI models?

To optimize inference latency, consider techniques like model quantization, pruning, and using efficient architectures.

What is model quantization?

Model quantization reduces the precision of the numbers used in the model, which can speed up inference without significant loss in accuracy.

Are there specific frameworks that support these optimizations?

Yes, frameworks like TensorFlow, PyTorch, and ONNX provide tools for model optimization, including quantization and pruning.

How much can I expect to reduce latency with these techniques?

The reduction in latency can vary, but optimizations can often lead to improvements of 2x to 10x, depending on the model and hardware.