Support Vector Machines
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
- AT&T,Bell Laboratories
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 1 September 1995
- Authors
- C Cortes, V Vapnik
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
- 100M
- Training data
- 60,000 tokens
Section 6.2.2: "...polynomials of degree 4 (that have more than 10^8 free parameters)..." They used 4-degree polynomials for MNIST
Section 6.2: "The large database consists of 60,000 training and 10,000 test patterns"
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
- Citations
- 48,968
Sources
Where this record came from and when it was last checked.
- Reference
- Support-Vector Networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Support Vector Machines was published by AT&T,Bell Laboratories, in the country recorded as United States of America, during September 1995. The publishing organisation is categorised as industry,Industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training consumed a corpus of around 60,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Support Vector Machines — common questions
Support Vector Machines— who created it?
It was published by AT&T,Bell Laboratories, based in United States of America, an organisation categorised as industry,Industry.
Support Vector Machines— when was it released?
It was published in September 1995. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Support Vector Machines— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Support Vector Machines— 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.
Support Vector Machines— 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.
Support Vector Machines— how many parameters does it have?
It has a parameter count of 100M. Section 6.2.2: "...polynomials of degree 4 (that have more than 10^8 free parameters)..." They used 4-degree polynomials for MNIST. 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.