These utilities shrink high-precision weights in large neural networks to lower bit-widths, significantly reducing memory footprint and accelerating inference speed. Use these tools to deploy complex architectures on edge devices or hardware with limited bandwidth without sacrificing excessive accuracy. When selecting a method, prioritize the balance between the compression ratio and the resulting degradation in performance metrics for your specific use case.
Run any LLM locally — quantize Hugging Face models to GGUF