What inference accelerations are achieved?

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

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

Depending on the task and approach, acceleration can range from several times to dozens of times compared to basic CPU inference.