NASv3 (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
- Google Brain
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 November 2016
- Authors
- Barret Zoph, Quoc V. Le
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Neural Architecture Search - NAS
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
- 37.4M
- Training data
- 45,000 tokens
Table 1
CIFAR-10 (does not factor in augmentation procedures)
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
- 2.2 × 10²¹ FLOP
- How it was established
- Third-party estimation,Operation counting
50 epochs * 50,000 images * 10.0 GFLOPSs * 12800 networks * 2 add-multiply * 3 backward pass = 1.9e6 PF = 22 pfs-days source: https://openai.com/blog/ai-and-compute/
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 800
- Compute cost
- $21,184
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited
- Record confidence
- Likely
- Citations
- 5,894
Sources
Where this record came from and when it was last checked.
- Reference
- Neural Architecture Search with Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
About this model
NASv3 (CIFAR-10) was published by Google Brain, in United States of America, in November 2016. The organisation is categorised as industry.
It works in Vision, and is recorded as doing image classification, Neural Architecture Search - NAS.
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
Producing it required around 2.2 × 10²¹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 45,000 tokens.
Its inclusion criterion is highly cited.
Answers
NASv3 (CIFAR-10) — common questions
What GPU do I need to run NASv3 (CIFAR-10)?
None. NASv3 (CIFAR-10) 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 NASv3 (CIFAR-10) open source?
The licensing for NASv3 (CIFAR-10) was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does NASv3 (CIFAR-10) have?
NASv3 (CIFAR-10) has 37.4M parameters. Table 1. 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 NASv3 (CIFAR-10)?
NASv3 (CIFAR-10) was published by Google Brain, based in United States of America, categorised as industry.
When was NASv3 (CIFAR-10) released?
NASv3 (CIFAR-10) was published in November 2016. 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 NASv3 (CIFAR-10) used for?
NASv3 (CIFAR-10) works in Vision, and is recorded as handling image classification, Neural Architecture Search - NAS. 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 NASv3 (CIFAR-10)?
Around 2.2 × 10²¹ 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.