Quantum Computing
AI Infrastructure
A technique that reduces model size by converting floating-point weights to lower-precision formats. This significantly reduces memory usage and computational requirements.
Detailed Explanation
Quantization is an AI infrastructure technique that lowers model size and computational demands by converting high-precision floating-point weights into lower-precision formats, such as 8-bit integers. This process decreases memory usage, accelerates inference speed, and makes deploying models on resource-constrained devices feasible. Despite minimal accuracy loss, it maintains essential model performance for practical applications.
Use Cases
- •Optimize mobile AI apps by deploying quantized models to ensure faster inference with reduced memory usage.
Related Terms
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