Aeneas
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 DeepMind,University of Nottingham,University of Warwick,Athens University of Economics and Business,Google,University of Oxford
- Organisation type
- Industry,Academia,Academia,Academia,Industry,Academia
- Country
- United States of America, United Kingdom of Great Britain and Northern Ireland, Greece
- Published
- 23 July 2025
- Authors
- Yannis Assael, Thea Sommerschield, Alison Cooley, Brendan Shillingford, John Pavlopoulos, Priyanka Suresh, Bailey Herms, Justin Grayston, Benjamin Maynard, Nicholas Dietrich, Robbe Wulgaert, Jonathan Prag, Alex Mullen, Shakir Mohamed
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Multimodal, Language
- Task
- Character recognition (OCR), Visual question answering
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.
- Training data
- tokens
"batch size of 1,024 text–image pairs, using the LAMB81 optimizer. The learning rate follows a schedule with a peak value of 3 × 10−3, a warm-up phase of 4,000 steps and a total of 1 million steps." -> ~10^9 image-text pairs
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
- 2.3 × 10²¹ FLOP
- How it was established
- Hardware
197000000000000 FLOP / chip / sec * 64 chips * 168 hours * 3600 sec / hour * 0.3 [assumed utilization] = 2.2875955e+21 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 v5e
- Chips used
- 64
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 28.2 kW
- Cloud vendor
- Google Cloud
"The model was trained for one week using 64 Tensor Processing Unit v5e chips on the Google Cloud platform"
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
- Hosted access (no API)
- Training code
- Open source
Apache 2.0 https://github.com/google-deepmind/predictingthepast/tree/main/train
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Contextualizing ancient texts with generative neural networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
Aeneas was published by Google DeepMind,University of Nottingham,University of Warwick,Athens University of Economics and Business,Google,University of Oxford, in the country recorded as United States of America, during July 2025. The category the publisher falls under is industry,Academia,Academia,Academia,Industry,Academia.
It works in the domain of Vision, Multimodal, Language, and is recorded as performing the task of character recognition (OCR), Visual question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required arithmetic totalling around 2.3 × 10²¹ FLOP, on hardware recorded as Google TPU v5e. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Aeneas — common questions
Aeneas— what is it used for?
It works in the domain of Vision, Multimodal, Language, and is recorded as handling the task of character recognition (OCR), Visual question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
Aeneas— how much compute was used to train it?
Training consumed around 2.3 × 10²¹ FLOP, on hardware recorded as Google TPU v5e. 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.
Aeneas— what GPU do I need to run it?
None. This 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.
Aeneas— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Aeneas— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Aeneas— who created it?
It was published by Google DeepMind,University of Nottingham,University of Warwick,Athens University of Economics and Business,Google,University of Oxford, based in United States of America, an organisation categorised as industry,Academia,Academia,Academia,Industry,Academia.
Aeneas— when was it released?
It was published in July 2025.
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.