Large Language Models (LLMs) often require substantial computational resources, making them challenging to run on devices without powerful GPUs. Quantization is a technique that reduces the memory footprint and computational requirements by converting higher-precision weights to lower-precision formats, such as FP32 to INT8. This post delves into various quantization methods, including Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), and reviews state-of-the-art techniques like LLM.int8(), GPTQ, and QLoRA. These methods help enable LLM deployment on edge devices without significant performance loss.
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