Print Recognition Logic
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
- IBM
- Organisation type
- Industry
- Country
- United States of America
- Published
- 1 January 1963
- Authors
- L. Kamentsky, Chao-Ning Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR)
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.
- Training data
- tokens
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
- 2.3 × 10⁷ FLOP
- How it was established
- Hardware
0.5*2.5*60*60*5000 = 22500000 = 2.25e7 Assumed utilization of 0.5 Trained for 2-3h on an IBM 7090 (from Introduction) Estimated IBM 7090 at 5000 FLOP/s based on multiplications per second (Nordhaus, 2007) Note: the Nordhaus estimate is very different from Wikipedia's estimate of 100000 FLOP/s, which cites a PowerPoint as source.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 3 hours
2-3h (from Introduction)
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
- Historical significance
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Computer-Automated Design of Multifont Print Recognition Logic
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Print Recognition Logic was published by IBM, in the country recorded as United States of America, during January 1963. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of character recognition (OCR).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 2.3 × 10⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It is tracked in the underlying dataset for one reason in particular: historical significance.
Answers
Print Recognition Logic — common questions
Print Recognition Logic— how much compute was used to train it?
Training consumed around 2.3 × 10⁷ 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.
Print Recognition Logic— 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.
Print Recognition Logic— 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.
Print Recognition Logic— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Print Recognition Logic— who created it?
It was published by IBM, based in United States of America, an organisation categorised as industry.
Print Recognition Logic— when was it released?
It was published in January 1963. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Print Recognition Logic— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of character recognition (OCR). 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.