JEST++
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 25 June 2024
- Authors
- Talfan Evans, Nikhil Parthasarathy, Hamza Merzic, Olivier J. Henaff
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 131,072,000,000,000 tokens
4b training examples seen (figure 1) The vision encoder takes images resized to (256 x 256) and the text-encoder tokenizes text with the sentencepiece tokenizer [26] trained on the English C4 dataset [39]. We crop the text to the first 64 tokens. visual encoder: ViT-B/16 4*10^9*((256/16)^2 + 64) = 1.28e+12 tokens (text and image)
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
- 1.9 × 10²¹ FLOP
- How it was established
- Other
1.9*10^21 from figure 1 and table 1 there is slight disrepancy (within confidence interval) between sigclip/jest++ training compute ratio if we compare epoch's estimated compute and table 1
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
- 256
- Power draw
- 113.7 kW
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
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 30
Sources
Where this record came from and when it was last checked.
- Reference
- Data curation via joint example selection further accelerates multimodal learning
- Last updated
- 25 May 2026
What the numbers mean
Background
JEST++ was published by Google DeepMind, in the country recorded as United States of America, during June 2024. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required arithmetic totalling around 1.9 × 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.
It was trained on a corpus of about 131,072,000,000,000 tokens of text.
Answers
JEST++ — common questions
JEST++— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
JEST++— 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.
JEST++— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
JEST++— when was it released?
It was published in June 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
JEST++— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
JEST++— how much compute was used to train it?
Training consumed around 1.9 × 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.
JEST++— 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.
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.