Millised inference kiirendused saavutatakse?

The acceleration of inference in machine learning depends on the optimization methods used and the hardware. Main approaches include:

• Model quantization — reducing the precision of weights (e.g., from float32 to int8), which decreases the volume of calculations and speeds up inference without significant loss of quality.
• Use of specialized libraries and frameworks (TensorRT, ONNX Runtime, OpenVINO), which optimize the computation graph and utilize hardware capabilities.
• Use of hardware acceleration: GPU, TPU, FPGA, or specialized neural processors.
• Parallelization and batching of requests — processing multiple requests simultaneously to increase throughput.
• Optimization of model architecture — simplifying the model, reducing the number of parameters.

Depending on the task and approach, acceleration can reach from a few times to dozens of times compared to basic CPU inference.