Large scale model quantization is a technique for compressing model parameters from high precision (such as FP16) to low precision (such as INT4, INT8), which can significantly reduce model storage and inference costs. It is a key technology for deploying large models locally.
##GGUF: Llama.cpp Ecological Standard Format
GGUF is a model format standard launched by the llama.cpp project. It supports multiple quantization levels (Q2_K to Q8-0) and has extremely high inference efficiency on the CPU. The advantage of GGUF lies in its extensive hardware compatibility and active community ecosystem, with almost all open-source models having corresponding versions of GGUF.
##GPT: Efficient Quantization on GPU
GPT Post Keeping Quantization (GPTQ) is a quantification method based on the optimal brain surgery framework that minimizes quantization errors to maintain model quality. The GPTQ quantized model has fast inference speed on GPUs, making it particularly suitable for scenarios that require high throughput.
##AWQ: Activate Perception Quantification
AWQ (Activation Aware Weight Quantization) is a quantitative method for perceiving the distribution of activation values, which can better preserve the key parameters of the model in inference. At the same level of quantization, AWQ typically achieves better model quality than GPTQ.
【 Reference sources 】 Llama.cpp official documents, GPTQ papers, AWQ papers, and related open source projects.