AI model quantization is a technique used to optimize neural networks by reducing the precision of the numbers representing their weights and activations. By moving from higher-precision formats like32-bit FP32 or 16-bit FP16) to lower-precision integers (such as 8-bit INT8) models can consume less memory, transfer data faster, and run more efficiently on hardware.

• FP32 - 32-bit floating point (full precision) Baseline accuracy, higher memory and compute cost.
• FP16 – 16-bit floating point (half-precision). Lower memory, faster arithmetic with some precision loss.
• BF16 – 16-bit floating point with the same dynamic range as FP32 but reduced precision. A good balance for Machine Learning workloads.
• INT16 - 16-bitinteger. Common for weights/activations in scenarios needing higher memory and speed, with the potential for accuracy trade-offs.
• INT8 – Strong memory and compute benefits; often requires careful calibration or quantization-aware training to maintain accuracy.

AI model quantization significantly reduces a model's memory footprint, enabling more efficient storage and transmission. By utilizing lower-precision arithmetic (such as FP16, BF16, INT8, or INT16), it accelerates inference and lowers computational demand. While accuracy trade-offs can occur, advanced techniques continually minimize this impact. AmpereOne® processors, for instance, natively support these formats, facilitating mixed precision AI workloads offering a practical pathway for deploying faster, more scalable, and cost-effective AI solutions.

• AmpereOne Family product brief
• Benefits of the FP16 Data Format for Natural Language Processing (NLP)
• Description of the IEEE 754 Standard
• Description of Single Precision Floating Point Format
• Description of Half Precision Floating Point Format
• Description of Bfloat16 Floating Point Format
• Description of Integer Data Formats