A recent technical guide on MarkTechPost breaks down the landscape of LLM model formats, covering GGUF, GPTQ, AWQ, EXL2, and the newer EXL3. It notes that all these formats aim to solve the same problem—efficiently storing and running large models—but they do so in different ways.
The central insight is the separation between file containers and quantization methods. The guide explains how bits per weight and calibration influence model size and output quality. For example, lower bit widths reduce memory but may degrade accuracy, and calibration data helps tune quantization to minimize that loss.
Finally, the guide gives practical recommendations based on target hardware. It advises which format suits Macs, consumer GPUs, and production environments, acknowledging that no single format is universally best. Developers are encouraged to weigh their constraints on memory, speed, and fidelity.