GGUF vs GPTQ vs AWQ vs EXL2: LLM Model Formats Explained for 2026

A comprehensive technical breakdown compares the four dominant LLM quantization and serialization formats in active use in 2026: GGUF, GPTQ, AWQ, and EXL2, covering their trade-offs in inference speed, memory footprint, hardware compatibility, and toolchain support. With the proliferation of quantized open models across Hugging Face and community repositories, choosing the wrong format for a given deployment target can mean significant performance gaps or hardware incompatibility. The guide is directly actionable for developers selecting formats for local inference, edge deployment, or self-hosted API serving, where format choice affects which runtimes, quantization tools, and GPU backends are available. As models like Ternary Bonsai 2 and others push new quantization approaches, understanding the format landscape is increasingly a core competency for any team doing local or on-premises LLM deployment.