GGUF vs GPTQ vs AWQ vs EXL2: LLM Model Formats Explained (2026)

MarkTechPost · 3 weeks ago · 18

The article explains how GGUF, GPTQ, AWQ, EXL2, and related LLM model formats differ by separating “containers” (how tensors are stored) from quantization methods (how weights are squeezed into fewer bits). It gives a memory rule of thumb for weights: weight memory ≈ parameters × bits-per-weight ÷ 8, noting that 70B at ~4.5 bits per weight is about 39 GB for weights only. It changes readers’ comparisons by making them treat most formats as a pairing of a storage format plus a quantization scheme rather than a single competing format, and it adds security and runtime implications like safetensors being non-executable compared with Python pickle.