AlexNet
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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 30 September 2012
- Authors
- Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
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
- 60M
- Training data
- 2,457,600,000 tokens
"Our neural network architecture has 60 million parameters."
"ImageNet is a dataset of over 15 million labeled high-resolution images belonging to roughly 22,000 categories. The images were collected from the web and labeled by human labelers using Amazon’s Mechanical Turk crowd-sourcing tool. Starting in 2010, as part of the Pascal Visual Object Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, …
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
- 4.7 × 10¹⁷ FLOP
- How it was established
- Operation counting,Hardware,Third-party estimation
1.2M images * 90 epochs * 0.75 GFLOP * (2 add-multiply) * (3 backward pass) = 470 PF = 0.0054 pfs-days Source: https://openai.com/blog/ai-and-compute/ Hardware method: 2 GTX 580 3GB GPUs for "between five and six days". Assuming 5.5 days and 32-bit training: 1.581 TFLOPS * 5.5 days * 2 = 1.5e18 FLOP Comparing to the operation counting method, this implies around 31% MFU. Authors of "AI and Memory Wall" (https://github.com/amirgholami/ai_and_memory_wall) estimated model's training compute as …
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 580
- Wall-clock time
- 132 hours
- Hardware utilisation
- MFU 31.3%
- Compute cost
- $16
"Our network takes between five and six days to train on two GTX 580 3GB GPUs."
Operation counting approach for compute estimate implies 470 PFLOP. Their hardware, 2 GTX 580 GPUs, would perform 1500 PFLOPs if operating in 32-bit for 5.5 days. This implies a model FLOP utilization rate of 31.3%. MFU = 470 PFLOP / 1500 PFLOP = 0.3133
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,Historical significance
- Record confidence
- Confident
- Citations
- 125,497
Sources
Where this record came from and when it was last checked.
- Reference
- ImageNet Classification with Deep Convolutional Neural Networks
- Last updated
- 1 January 2026
What the numbers mean
Background
AlexNet was published by University of Toronto, in Canada, in September 2012. academia is the category the publisher falls under.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required around 4.7 × 10¹⁷ FLOP of arithmetic, on NVIDIA GeForce GTX 580, which is a statement about the training budget rather than about inference.
Around 2,457,600,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: highly cited,Historical significance.
Answers
AlexNet — common questions
Who created AlexNet?
AlexNet was published by University of Toronto, based in Canada, categorised as academia.
When was AlexNet released?
AlexNet was published in September 2012. 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 AlexNet used for?
AlexNet works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train AlexNet?
Around 4.7 × 10¹⁷ FLOP, on NVIDIA GeForce GTX 580. 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 AlexNet?
None. AlexNet 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 AlexNet open source?
The licensing for AlexNet 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 AlexNet have?
AlexNet has 60M parameters. "Our neural network architecture has 60 million parameters.". 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.
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.