What is Quantization

Quantization reduces machine-learning model memory requirements by representing parameters with lower-precision formats such as 8-bit or 4-bit values rather than float32 or float16, enabling large models such as Llama 2 and SDXL to run on memory-constrained edge and consumer hardware. Because it compresses values through specialized representations rather than merely removing bits, quantization can affect model accuracy and should be evaluated in its deployed form. Lightning Fabric supports bitsandbytes-based quantization through its `BitsandbytesPrecision` plugin, with int8 and int8-training modes for 8-bit inference and training, respectively, and NF4 or FP4 modes for 4-bit quantization. The QLoRA approach demonstrates that NF4 can support efficient fine-tuning of very large language models while retaining performance comparable to 16-bit fine-tuning, and its double-quantization option further reduces memory use by quantizing internal quantization constants through NF4-DQ or FP4-DQ modes.

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