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# GGUF and GPTQ solve different problems: containers versus quantization
- URL: https://globalfeed.ai/en/gguf-and-gptq-solve-different-problems-containers-versus-quantization/
- Published: 2026-09-21T22:07:16.000Z
- Updated: 2026-09-21T22:07:16.000Z
- Description: A MarkTechPost explainer separates model file containers from weight quantization methods, covering GGUF, GPTQ, AWQ and EXL2.
- Author: GlobalFeed Editor
- Tags: GGUF, quantization, Hugging Face, x-ggerganov, x-huggingface, dil-en

MarkTechPost has published a breakdown of a distinction that trips up people running models locally: a container defines how tensors sit on disk, while a quantization method defines how weights are squeezed into fewer bits. The piece covers weights only; KV cache and runtime overhead add more on top.

Unquantized models usually ship as 16-bit weights in **pytorch\_model.bin** or **model.safetensors**. Older .bin and .pt files rely on Python pickle, so loading one can execute arbitrary code, making untrusted checkpoints a security risk. Safetensors, created at Hugging Face, removes that: a small JSON header plus raw tensor buffers, nothing executable inside. It is now listed as a PyTorch Foundation project. Most GPTQ, AWQ, EXL2, EXL3 and MLX models also live in .safetensors files, because the quantization sits in the tensor contents and a config file, not in a new container.

## What GGUF changed

GGUF is a binary format for GGML-based executors such as llama.cpp. It was created by Georgi Gerganov, who also leads llama.cpp, and arrived on 21 August 2023 to replace GGML. The older GGML, GGMF and GGJT files could not state a model's architecture, and adding a hyperparameter broke every existing file. GGUF moved to typed key-value metadata. The spec lists five goals: single-file deployment, extensibility, mmap compatibility, easy loading and complete information inside the file. It can also carry the tokenizer, special tokens and a Jinja chat template.

Suffixes such as Q4\_K\_M name the scheme. For Q4\_K: a super-block holds 256 weights, 1,024 bits of weights, 96 bits of scales and minimums, plus a 16-bit super-scale and super-minimum, totalling 1,152 bits, or 4.5 bits per weight. The \_S, \_M and \_L labels are mixes rather than new types; llama.cpp describes Q4\_K\_M as using Q6\_K for half of the attention.wv and feed\_forward.w2 tensors.

## GPTQ, AWQ and EXL2

GPTQ came from Elias Frantar, Saleh Ashkboos, Torsten Hoefler and Dan Alistarh, appeared on arXiv on 31 October 2022 and was published at ICLR 2023\. It is one-shot post-training quantization that uses approximate second-order information to decide rounding, compensating error in one column with weights not yet quantized.

AWQ came from Song Han's group at MIT, reached arXiv on 1 June 2023 and won the MLSys 2024 Best Paper Award. It identifies salient channels through activation magnitudes and scales them via an equivalent transformation instead of storing them at higher precision.

EXL2 is the native format of turboderp's ExLlamaV2, which is why filenames read 4.65bpw. Its successor EXL3 builds on QTIP from Cornell RelaxML. Full details are in the [MarkTechPost article](https://www.marktechpost.com/2026/09/18/gguf-vs-gptq-vs-awq-vs-exl2-llm-model-formats-explained-2026/?ref=globalfeed.ai).