ViT-G/14
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 Brain,Google Research
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 8 June 2021
- Authors
- X Zhai, A Kolesnikov, N Houlsby, L Beyer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Approach
- Self-supervised learning
- Numerical format
- BF16
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
- 1.8B
- Training data
- 3,000,000,000 tokens
- Epochs
- 54.6
- Batch size
- 32,768
Table 2 of paper
"For this study, we use the proprietary JFT-3B dataset, a larger version of the JFT-300M dataset used in many previous works on large-scale computer vision models [31, 18, 11]. This dataset consists of nearly 3 billion images, annotated with a class-hierarchy of around 30k labels via a semi-automatic pipeline" Epochs: 5M steps (Table 11) * 32768 (batch size) / 3B = 54.6 epochs
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
- 5.9 × 10²² FLOP
- How it was established
- Hardware,Operation counting
Digitizing Figure 9 indicates training used 27,200 TPUv3 core-days. TPUv3 is 123 teraflop/s per chip, 2 cores per chip. 1.23e14 * (1/2) * 27,200 * 24 * 3600 * 0.4 = 5.78e22 Alternatively, Table 2 indicates 965.3e9 FLOPs per forward pass on a 224^2 image. Table 4 indicates 5 million steps at a (normalized) batch size of 4096, and total flops including backward pass would be 3x the FLOPs from forward passes alone, so we get: 4096 * 5e6 * 965.3e9 * 3 = 5.93e22 (Note that actual batch size appear…
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
- Chips used
- 2,048
- Power draw
- 1.9 MW
- Compute cost
- $3,848
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
About the weights: https://twitter.com/giffmana/status/1402507421029916672 About the code: Apache 2.0 https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/scaling_laws/train_vit_g.py
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 1,393
"we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy"
Sources
Where this record came from and when it was last checked.
- Reference
- Scaling Vision Transformers
- Last updated
- 25 May 2026
What the numbers mean
What this model is
ViT-G/14 was published by Google Brain,Google Research, in United States of America, in June 2021. The organisation is categorised as industry,Industry.
It works in Vision, and is recorded as doing image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 5.9 × 10²² FLOP of arithmetic, on Google TPU v3, which is a statement about the training budget rather than about inference.
It was trained on about 3,000,000,000 tokens of text.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
ViT-G/14 — common questions
When was ViT-G/14 released?
ViT-G/14 was published in June 2021. 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 ViT-G/14 used for?
ViT-G/14 works in Vision, and is recorded as handling 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.
How much compute was used to train ViT-G/14?
Around 5.9 × 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 ViT-G/14?
None. ViT-G/14 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 ViT-G/14 open source?
No. ViT-G/14 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ViT-G/14 have?
ViT-G/14 has 1.8B parameters. Table 2 of paper. 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.
Who created ViT-G/14?
ViT-G/14 was published by Google Brain,Google Research, based in United States of America, categorised as industry,Industry.
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.