Predictive Coding NN
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
- Technical University of Munich
- Organisation type
- Academia
- Country
- Germany
- Published
- 2 December 1994
- Authors
- J. Schmidhuber, Stefan Heil
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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
- 206.9K
- Training data
- 600,000 tokens
- Epochs
- 25
5*80*430+430+430*80+80=206910 "P has nk input units and k output units. n is called the "time-window size" "Note that the time-window was quite small (n = 5)." "alphabet consisted of k = 80 possible characters" "P had 430 hidden units"
Training dataset: 15000*40=600000 "The training set for the predictor was given by a set of 40 articles from the newspaper Miinchner M erkur, each containing between 10000 and 20000 characters."
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
- 1.9 × 10¹³ FLOP
- How it was established
- Operation counting
2*206910*3*15000000=18621900000000=1.86e13 "The training phase consisted of 25 sweeps through the training set"
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
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Predictive Coding with Neural Nets: Application to Text Compression
- Last updated
- 28 November 2025
What the numbers mean
Background
Predictive Coding NN was published by Technical University of Munich, in Germany, in December 1994. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 1.9 × 10¹³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 600,000 tokens went into training it.
Its inclusion criterion is historical significance.
Answers
Predictive Coding NN — common questions
When was Predictive Coding NN released?
Predictive Coding NN was published in December 1994. 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 Predictive Coding NN used for?
Predictive Coding NN works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Predictive Coding NN?
Around 1.9 × 10¹³ 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 Predictive Coding NN?
None. Predictive Coding NN 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 Predictive Coding NN open source?
The licensing for Predictive Coding NN 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 Predictive Coding NN have?
Predictive Coding NN has 206.9K parameters. 5*80*430+430+430*80+80=206910 "P has nk input units and k output units. n is called the "time-window size" "Note that the time-window was quite small (n = 5)." "alphabet consisted of k = 80 possible characters" "P had 430 hidden units". 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 Predictive Coding NN?
Predictive Coding NN was published by Technical University of Munich, based in Germany, categorised as academia.
The other direction
Looking at it from the other side?
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