JEST-L++
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- 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
- 98,304,000,000,000 tokens
- Batch size
- 32,000
"Our default training configuration follows that of SigLIP [54], with a ViT-B/16 and Bert-B image-text dual encoder, training on WebLI for 3 billion examples with a batch size of 32k and the sigmoid-contrastive loss... We split training across 256 TPUv5e chips," according to page 14 of https://arxiv.org/pdf/2406.17711v1.
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 × 10²¹ FLOP
See the right subfigure of Figure 1 on page 2 of https://arxiv.org/pdf/2406.17711v1.
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
(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] We do not explicitly provide the code for our experiments, however we detail in pseudocode the main components of our method (see algorithms 1 and A.1). Our model implementations and base experimental configuration were adopted from the open source big_vision codebase [3]. We do not provide the reference or downstream training datasets, bu…
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
Where it came from
JEST-L++ was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, 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.
How it was trained
Training it took a computation budget of roughly 2 × 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.
The training set ran to roughly 98,304,000,000,000 tokens of text.
Answers
JEST-L++ — common questions
JEST-L++— how much compute was used to train it?
Training consumed around 2 × 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-L++— 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.
JEST-L++— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
JEST-L++— 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-L++— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
JEST-L++— 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-L++— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.