ADAM (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 Amsterdam,OpenAI,University of Toronto
- Organisation type
- Academia,Industry,Academia
- Country
- Netherlands, United States of America, Canada
- Published
- 22 December 2014
- Authors
- Diederik P. Kingma, Jimmy Ba
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
- 2.4M
- Training data
- 50,000 tokens
CIFAR-10 with c64-c64-c128-1000 architecture "Our CNN architecture has three alternating stages of 5x5 convolution filters and 3x3 max pooling with stride of 2 that are followed by a fully connected layer of 1000 rectified linear hidden units (ReLU’s). " "Training cost over 45 epochs. CIFAR-10 with c64-c64-c128-1000 architecture." CIFAR-10's input dimension is 32x32x3 Parameter: 3*64*5*5+64*64*5*5+64*128*5*5+128*4*4*1000+1000*10=2370000
Assumed they used the standard CIFAR-10 training split
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
- 6.2 × 10¹⁴ FLOP
- How it was established
- Third-party estimation,Operation counting
From https://openai.com/blog/ai-and-compute/ Appendix less than 0.0007 pfs-days (86400*10^15*0.0007) Manual estimate: 3 (forward-backward adjustment) * 92589600 flop per forward pass * 50000 examples * 45 epochs = 624979800000000 (6.25e14)
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.
- Why it is tracked
- Highly cited
- Record confidence
- Confident
- Citations
- 166,267
Sources
Where this record came from and when it was last checked.
- Reference
- Adam: A Method for Stochastic Optimization
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ADAM (CIFAR-10) was published by University of Amsterdam,OpenAI,University of Toronto, in the country recorded as Netherlands, during December 2014. The publishing organisation is categorised as academia,Industry,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 6.2 × 10¹⁴ FLOP. 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 50,000 tokens of text.
The reason it appears in this catalogue at all: highly cited.
Answers
ADAM (CIFAR-10) — common questions
ADAM (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.
ADAM (CIFAR-10)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
ADAM (CIFAR-10)— how many parameters does it have?
It has a parameter count of 2.4M. CIFAR-10 with c64-c64-c128-1000 architecture "Our CNN architecture has three alternating stages of 5x5 convolution filters and 3x3 max pooling with stride of 2 that are followed by a fully connected layer of 1000 rectified linear hidden units (ReLU’s). " "Training cost over 45 epochs. CIFAR-10 with c64-c64-c128-1000 architecture." CIFAR-10's input dimension is 32x32x3 Parameter: 3*64*5*5+64*64*5*5+64*128*5*5+128*4*4*1000+1000*10=2370000. 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.
ADAM (CIFAR-10)— who created it?
It was published by University of Amsterdam,OpenAI,University of Toronto, based in Netherlands, an organisation categorised as academia,Industry,Academia.
ADAM (CIFAR-10)— when was it released?
It was published in December 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ADAM (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.
ADAM (CIFAR-10)— how much compute was used to train it?
Training consumed around 6.2 × 10¹⁴ FLOP. 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.
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.