GQA-8-XXL
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 23 December 2023
- Authors
- Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, Sumit Sanghai
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text summarization, Language modeling/generation, Translation
- Base model
- T5-11B
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 11B
- Training data
- tokens
same as base model
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.5 × 10²² FLOP
- How it was established
- Hardware
3.3e+22 FLOP [base model] + 1.912896e+21 FLOP = 3.4912896e+22 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v3
- Chip-hours
- 14,400
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
Apache 2.0 https://github.com/google/flaxformer
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Confident
the paper introduced grouped-query attention
Sources
Where this record came from and when it was last checked.
- Reference
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- Last updated
- 28 November 2025
What the numbers mean
Background
GQA-8-XXL was published by Google Research, in United States of America, in December 2023. The organisation is categorised as industry.
It works in Language, and is recorded as doing text summarization, Language modeling/generation, Translation.
It is derived from T5-11B rather than trained from scratch, which is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required around 3.5 × 10²² FLOP of arithmetic, on Google TPU v3, which is a statement about the training budget rather than about inference.
It is tracked in the underlying dataset for one reason in particular: historical significance.
Answers
GQA-8-XXL — common questions
Who created GQA-8-XXL?
GQA-8-XXL was published by Google Research, based in United States of America, categorised as industry.
When was GQA-8-XXL released?
GQA-8-XXL was published in December 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is GQA-8-XXL used for?
GQA-8-XXL works in Language, and is recorded as handling text summarization, Language modeling/generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train GQA-8-XXL?
Around 3.5 × 10²² FLOP, on Google TPU v3. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run GQA-8-XXL?
None. GQA-8-XXL is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is GQA-8-XXL open source?
No. GQA-8-XXL has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GQA-8-XXL have?
GQA-8-XXL has 11B parameters. same as base model. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.