GoogLeNet / InceptionV1
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,University of Michigan,University of North Carolina
- Organisation type
- Industry,Academia,Academia
- Country
- United States of America
- Published
- 17 September 2014
- Authors
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
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
- 6.8M
- Training data
- 571,392,000,000 tokens
- Epochs
- 827
Computed summing the parameters on table 1 of section 5
"The ILSVRC 2014 classification challenge involves the task of classifying the image into one of 1000 leaf-node categories in the Imagenet hierarchy. There are about 1.2 million images for training, 50,000 for validation and 100,000 images for testing" ... "We participated in the challenge with no external data used for training."
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
- 1.5 × 10¹⁸ FLOP
- How it was established
- Third-party estimation
AI and Compute (https://openai.com/blog/ai-and-compute/) charts imply a value of 1.51e18 (value extracted using WebPlotDigitizer https://automeris.io/WebPlotDigitizer/ ). Based on the paper, there are 1.5B multiply-adds per inference, and 1.2M images in the training set, but an unknown number of epochs. They decrease the learning rate by 4% every 8 epochs, so there are likely many. If the figure from AI and Compute is taken as true, there were likely 140 epochs
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
- 47,229
Sources
Where this record came from and when it was last checked.
- Reference
- Going deeper with convolutions
- Last updated
- 25 May 2026
What the numbers mean
About this model
GoogLeNet / InceptionV1 was published by Google,University of Michigan,University of North Carolina, in United States of America, in September 2014. The organisation is categorised as industry,Academia,Academia.
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 1.5 × 10¹⁸ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
It was trained on about 571,392,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
GoogLeNet / InceptionV1 — common questions
How much compute was used to train GoogLeNet / InceptionV1?
Around 1.5 × 10¹⁸ 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.
What GPU do I need to run GoogLeNet / InceptionV1?
None. GoogLeNet / InceptionV1 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 GoogLeNet / InceptionV1 open source?
The licensing for GoogLeNet / InceptionV1 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 GoogLeNet / InceptionV1 have?
GoogLeNet / InceptionV1 has 6.8M parameters. Computed summing the parameters on table 1 of section 5. 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 GoogLeNet / InceptionV1?
GoogLeNet / InceptionV1 was published by Google,University of Michigan,University of North Carolina, based in United States of America, categorised as industry,Academia,Academia.
When was GoogLeNet / InceptionV1 released?
GoogLeNet / InceptionV1 was published in September 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.
What is GoogLeNet / InceptionV1 used for?
GoogLeNet / InceptionV1 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.
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.