SigLiT
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- 27 March 2023
- Authors
- Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer
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
- tokens
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
- 7.6 × 10¹⁹ FLOP
- How it was established
- Hardware
275000000000000 FLOP/s *48 hours *4 GPUs *3600 sec / hour *0.4 = 7.6032e+19 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 v4
- Chips used
- 4
- Wall-clock time
- 48 hours
- Power draw
- 2.7 kW
2 days = 48 hours
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
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
- Sigmoid Loss for Language Image Pre-Training
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SigLiT was published by Google DeepMind, in United States of America, in March 2023. The organisation is categorised as industry.
It works in Vision, and is recorded as doing image classification.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
How it was trained
The training run consumed about 7.6 × 10¹⁹ FLOP, on Google TPU v4. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
SigLiT — common questions
What is SigLiT used for?
SigLiT 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.
Where can I download SigLiT?
The weights for SigLiT are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train SigLiT?
Around 7.6 × 10¹⁹ FLOP, on Google TPU v4. 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 SigLiT?
We cannot say. SigLiT has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is SigLiT open source?
Its weights are published, so SigLiT can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
How many parameters does SigLiT have?
No parameter count has been published for SigLiT, which is why no memory or speed figure appears on this page.
Who created SigLiT?
SigLiT was published by Google DeepMind, based in United States of America, categorised as industry.
When was SigLiT released?
SigLiT was published in March 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.
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.