GELU for CIFAR-10
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
- University of California (UC) Berkeley,Toyota Technological Institute at Chicago
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 6 June 2023
- Authors
- Dan Hendrycks, Kevin Gimpel
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.
- Parameters
- 9.9K
- Training data
- 50,000 tokens
- Epochs
- 250
https://docs.google.com/spreadsheets/d/1rnsk2ysbAra1UfQWD9TFDki-0T9OmKqBdd7fw6O7QWE/edit?usp=sharing
50K - traning examples in MNIST datset
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
- 7.4 × 10¹¹ FLOP
- How it was established
- Operation counting
6ND = 6*9888*50000*250=741600000000
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
- NVIDIA GeForce GTX TITAN X
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.
- Training code
- Open source
https://github.com/hendrycks/GELUs MIT License
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Gaussian Error Linear Units (GELUs)
- Last updated
- 28 November 2025
What the numbers mean
What this model is
GELU for CIFAR-10 was published by University of California (UC) Berkeley,Toyota Technological Institute at Chicago, in the country recorded as United States of America, during June 2023. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
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 7.4 × 10¹¹ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 50,000 tokens of text.
Answers
GELU for CIFAR-10 — common questions
GELU for CIFAR-10— how many parameters does it have?
It has a parameter count of 9.9K. https://docs.google.com/spreadsheets/d/1rnsk2ysbAra1UfQWD9TFDki-0T9OmKqBdd7fw6O7QWE/edit?usp=sharing. 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.
GELU for CIFAR-10— who created it?
It was published by University of California (UC) Berkeley,Toyota Technological Institute at Chicago, based in United States of America, an organisation categorised as academia,Academia.
GELU for CIFAR-10— when was it released?
It was published in June 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.
GELU for CIFAR-10— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
GELU for CIFAR-10— how much compute was used to train it?
Training consumed around 7.4 × 10¹¹ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. 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.
GELU for CIFAR-10— 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.
GELU for CIFAR-10— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.