Verified claim · AI-ML · 100% confidence
GPTQ introduced in: Frantar et al. 2022 — accurate post-training quantization for GPT models.
Last verified 2026-05-16 · Methodology veritas-v0.1 · a9ab1ec12062f7ae
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Structured fields
- Subject
- GPTQ
- Predicate
introduced_in- Object
- Frantar et al. 2022 — accurate post-training quantization for GPT models
- Confidence
- 100%
- Tags
- gptq · quantization · ist-austria · inference · post-training · 2022 · introduced_in
Sources (2)
[1] preprint · arXiv (Frantar, Ashkboos, Hoefler, Alistarh / IST Austria) · 2022-10-31
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers“In this paper, we present a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits per weight, with negligible accuracy degradation relative to the uncompressed baseline.”
[2] github release · IST Austria DAS Lab · 2022-10-31
GPTQ — official IST-DASLab GitHub repository
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Evidence comes from 2 primary sources: arXiv (Frantar, Ashkboos, Hoefler, Alistarh / IST Austria), IST Austria DAS Lab. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/a9ab1ec12062f7ae.json includes an HMAC-SHA256 signature for audit verification.
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// "GPTQ introduced in: Frantar et al. 2022 — accurate post-training quantization for GPT models."Python
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# "GPTQ introduced in: Frantar et al. 2022 — accurate post-training quantization for GPT models."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_gptq_fact() -> dict:
"""Fetch the verified SourceScore claim for GPTQ."""
r = httpx.get("https://sourcescore.org/api/v1/claims/a9ab1ec12062f7ae.json")
return r.json()