ADALINE
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 30 June 1960
- Authors
- Widrow and Hoff
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Pattern recognition
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
- 0K
- Training data
- 100 tokens
"The machine's total experience is stored in the values of the weights a0,...,a16"
"The best system, arrived at by slow precise adaptation on the full body of 100 noisy patterns, was able to classify these patterns as desired except for twelve errors." https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf
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
- 6.6 × 10³ FLOP
- How it was established
- Operation counting
"The method of searching that has proven most useful is the method of steepest descent" Apparently each pattern was only shown once to the system. So the training compute is (forward pass compute) * (3 for backprop) * dataset size This is a single layer (and single neuron) which does not require gradients w.r.t. inputs - 1:1 forward-backward ratio
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
- Confident
- Citations
- 6,329
Sources
Where this record came from and when it was last checked.
- Reference
- Adaptive switching circuits
- Last updated
- 28 November 2025
What the numbers mean
About this model
ADALINE was published by Stanford University, in United States of America, in June 1960. The organisation is categorised as academia.
It works in Vision, and is recorded as doing pattern recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 6.6 × 10³ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 100 tokens of text.
Its inclusion criterion is highly cited.
Answers
ADALINE — common questions
What is ADALINE used for?
ADALINE works in Vision, and is recorded as handling pattern recognition. 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 ADALINE?
Around 6.6 × 10³ 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 ADALINE?
None. ADALINE 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 ADALINE open source?
The licensing for ADALINE 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 ADALINE have?
ADALINE has 0K parameters. "The machine's total experience is stored in the values of the weights a0,...,a16". 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 ADALINE?
ADALINE was published by Stanford University, based in United States of America, categorised as academia.
When was ADALINE released?
ADALINE was published in June 1960. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.