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Quantization

Quantization is a technique that reduces an AI model's numerical precision — for example, converting 16-bit floating-point weights to 4-bit integers — to shrink the model's memory footprint and speed up inference. While this trades a small amount of accuracy for major efficiency gains, well-implemented quantization can reduce model size by 75% or more with minimal impact on output quality.

Example

A 70-billion parameter model normally requires 140 GB of GPU memory in 16-bit precision. After 4-bit quantization, it fits in approximately 35 GB — running on a single high-end GPU instead of requiring multiple. The quantized model produces nearly identical responses for most tasks at 2-3x faster inference speed.

Related Terms

Large Language Model (LLM)InferenceTransformer

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