LLM Quantization Guide: GGUF vs AWQ vs GPTQ vs bitsandbytes Compared (2026)

Quantization is a technique used to compress large neural network models by reducing the precision of weights from 16-bit or 32-bit floats to smaller bit sizes, such as 4-bit integers, thus significantly reducing memory requirements while maintaining most of the model's quality. This process allows models, which traditionally require powerful hardware like multiple A100 GPUs, to run on more accessible hardware, such as a single RTX 4090. Different quantization methods cater to specific hardware and use cases: GGUF is optimal for CPU-based inference, AWQ excels in throughput on NVIDIA GPUs with the Marlin kernel, GPTQ offers a mature option for GPU inference with a strong pre-existing model library, and bitsandbytes supports dynamic quantization during training with features like QLoRA for fine-tuning. Each method has distinct advantages and limitations, such as GGUF's suitability for hybrid CPU/GPU inference and AWQ's focus on inference speed. The selection of a quantization method depends on factors like the hardware used, the need for speed versus quality, and the specific application requirements, with each method offering varying trade-offs between quality retention and efficiency.

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