A Guide to Quantization in LLMs

Quantization is a model compression technique that reduces the size of Large Language Models (LLMs) by converting their weights and activations from high-precision data representation to lower-precision data representation, making them more portable and scalable. This process enables LLMs to run on a wider range of devices, including single GPUs or even CPUs, while reducing memory consumption, storage space, energy efficiency, and inference time. Quantization techniques can be categorized into Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). Some popular LLM quantization methods include QLoRA, PRILoRA, GPTQ, GGML/GGUF, and AWQ. These techniques help to increase the adoption of LLMs by reducing their memory requirements and enabling them to run on a broader range of hardware.

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