Inception v3
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 College London (UCL)
- Organisation type
- Industry,Academia
- Country
- United States of America, United Kingdom of Great Britain and Northern Ireland
- Published
- 2 December 2015
- Authors
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
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
- 23.6M
- Training data
- 1,200,000 tokens
Table 3 from Xception paper
The full dataset is a lot larger and has far more categories. When people say "ImageNet" they're usually referring to the subset of the full dataset with 1000 categories and 1.2million images, found here: https://image-net.org/challenges/LSVRC/2012/
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 × 10²⁰ FLOP
- How it was established
- Third-party estimation
Authors of "AI and Memory Wall" (https://github.com/amirgholami/ai_and_memory_wall) estimated model's training compute as 100,000 PFLOP = 1*10^20 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Compute cost
- $1,218
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
- 30,915
Sources
Where this record came from and when it was last checked.
- Reference
- Rethinking the inception architecture for computer vision.
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Inception v3 was published by Google,University College London (UCL), in the country recorded as United States of America, during December 2015. It comes out of an organisation categorised as 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.
What went into building it
The training run consumed about 1 × 10²⁰ 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 1,200,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
Inception v3 — common questions
Inception v3— how much compute was used to train it?
Training consumed around 1 × 10²⁰ 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.
Inception v3— 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.
Inception v3— 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.
Inception v3— how many parameters does it have?
It has a parameter count of 23.6M. Table 3 from Xception paper. 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.
Inception v3— who created it?
It was published by Google,University College London (UCL), based in United States of America, an organisation categorised as industry,Academia.
Inception v3— when was it released?
It was published in December 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Inception v3— 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.
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.