Quantize Llama models with GGML and llama.cpp





Due to the massive size of Large Language Models (LLMs), quantization has become an essential technique to run them efficiently. By reducing the precision of their weights, you can save memory and speed up inference while preserving most of the model’s performance. Recently, 8-bit and 4-bit quantization unlocked the possibility of running LLMs on consumer hardware. Coupled with the release of Llama models and parameter-efficient techniques to fine-tune them (LoRA, QLoRA), this created a rich ecosystem of local LLMs that are now competing with OpenAI’s GPT-3.5 and GPT-4.

Besides the naive approach covered in this article, there are three main quantization techniques: NF4, GPTQ, and GGML. NF4 is a static method used by QLoRA to load a model in 4-bit precision to perform fine-tuning. In a previous article, we explored the GPTQ method and quantized our own model to run it on a consumer GPU. In this article, we will introduce the GGML technique, see how to quantize Llama models, and provide tips and tricks to achieve the best results.