---
title: 'GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints'
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/2305-13245-gqa.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai, GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (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, attention
arxiv_id: 2305.13245
alias: 2305.13245-gqa
sources:
  - id: S1
    title: 'GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints'
    publisher: arXiv
    authors: 'Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai'
    url: https://arxiv.org/abs/2305.13245
    version_read: 'v3 (23 Dec 2023)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

## Summary
Grouped-query attention (GQA) is a generalization of multi-query attention (MQA). MQA uses a single key-value head and can speed up decoder inference, but it can reduce quality. GQA uses an intermediate number of key-value heads: more than one, but fewer than the number of query heads. The authors also describe a recipe for uptraining existing multi-head checkpoints into MQA or GQA models using 5% of original pre-training compute. They report that uptrained GQA achieves quality close to multi-head attention with speed comparable to MQA. [S1]

## Claims
- id: C1
  claim: 'GQA is a generalization of multi-query attention that uses an intermediate number of key-value heads.'
  source: S1
  evidence: "a generalization of multi-query attention"
- id: C2
  claim: 'They propose uptraining existing multi-head checkpoints using 5% of original pre-training compute.'
  source: S1
  evidence: "using 5% of original pre-training compute"
- id: C3
  claim: 'They report that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.'
  source: S1
  evidence: "uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA"

## Verification
- Code available: The authors say they implemented experiments with JAX, Flax, and Flaxformer (https://github.com/google/flaxformer, HTTP 200 on 2026-11-11). No separate GQA repository was named as this paper's release. Not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live notes 2309-06180-pagedattention-vllm and 2401-04088-mixtral-of-experts. This is the paper-verification note for GQA itself.
- Limits: Quality and speed claims are from uptrained T5-scale experiments in the paper, not a guarantee for every later decoder.
- 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 22 May 2023 (v1); last revised 23 Dec 2023 (v3). Sources: [S1] https://arxiv.org/abs/2305.13245, v3 (23 Dec 2023). checked_on: 2026-11-11.

## When to apply
Use when reducing key-value cache cost at inference without fully collapsing to a single MQA head, or when converting an existing multi-head checkpoint.

## Facts
- id: F1
  claim: 'GQA uses an intermediate number of key-value heads, between one (MQA) and the full number of query heads.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors describe uptraining existing multi-head checkpoints with 5% of original pre-training compute.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They report that uptrained GQA is close in quality to multi-head attention and comparable in speed to MQA.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2305.13245, https://arxiv.org/abs/2305.13245. License: CC BY 4.0. v3 (23 Dec 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.

---

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