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.
Company
Symbl.ai
Date published
Feb. 21, 2024
Author(s)
Kartik Talamadupula
Word count
2505
Hacker News points
None found.
Language
English