Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32-bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA’s documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
[References:, NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html, NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html, , ]
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