---
title: 'GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2210-17323-gptq.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh, GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked; code not run.'
category: arxiv
topic_area: 'ai-research: efficiency'
tags: arxiv, efficiency, quantization
arxiv_id: 2210.17323
alias: 2210.17323-gptq
sources:
  - id: S1
    title: 'GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers'
    publisher: arXiv
    authors: 'Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh'
    url: https://arxiv.org/abs/2210.17323
    version_read: 'v2 (22 Mar 2023)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

## Summary
GPTQ is a one-shot post-training weight quantization method that uses approximate second-order information. The authors report that it can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing bitwidth to 3 or 4 bits per weight with negligible accuracy degradation relative to the uncompressed baseline. They also report end-to-end inference speedups over FP16 of around 3.25x on NVIDIA A100 GPUs and 4.5x on NVIDIA A6000 GPUs. [S1]

## Claims
- id: C1
  claim: 'GPTQ is a one-shot weight quantization method based on approximate second-order information.'
  source: S1
  evidence: "a new one-shot weight quantization method based on approximate second-order information"
- id: C2
  claim: 'They report quantizing 175 billion parameter models in approximately four GPU hours to 3 or 4 bits per weight.'
  source: S1
  evidence: "reducing the bitwidth down to 3 or 4 bits per weight"
- id: C3
  claim: 'They report about 3.25x speedup on A100 and 4.5x on A6000 versus FP16.'
  source: S1
  evidence: "around 3.25x when using high-end GPUs (NVIDIA A100)"

## Verification
- Code available: Paper: https://github.com/IST-DASLab/gptq, which returned HTTP 200 on 2026-11-11. Not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live note 2305-14314-qlora. This is the paper-verification note for GPTQ itself.
- Limits: Accuracy and speedup figures are the authors' reported results on the models and hardware in the v2 paper (revised 22 Mar 2023). Extreme 2-bit or ternary settings are described as providing only reasonable accuracy, not as matching the baseline.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 31 Oct 2022 (v1); last revised 22 Mar 2023 (v2). Sources: [S1] https://arxiv.org/abs/2210.17323, v2 (22 Mar 2023). checked_on: 2026-11-11.

## When to apply
Use when compressing a trained generative Transformer for inference, as distinct from quantization-aware training methods such as QLoRA.

## Facts
- id: F1
  claim: 'GPTQ is a one-shot post-training quantization method using approximate second-order information.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors report quantizing 175 billion parameter models to 3 or 4 bits per weight in about four GPU hours.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They report inference speedups over FP16 of about 3.25x on A100 and 4.5x on A6000 GPUs.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2210.17323, https://arxiv.org/abs/2210.17323. License: CC BY 4.0. v2 (22 Mar 2023). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

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Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).