Delta RNN (+ full context)
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
- IDSIA,SUPSI,King Abdullah University of Science and Technology (KAUST)
- Organisation type
- Academia,Academia,Academia
- Country
- Switzerland, Saudi Arabia
- Published
- 11 June 2021
- Authors
- Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber
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
- 44.6M
- Training data
- 103,000,000 tokens
- Epochs
- 40
44.6M
"All models are trained and evaluated on the span of 256 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
- 1.1 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 44600000 parameters * 103000000 tokens * 40 epochs [assumed -> "Speculative" confidence] = 1.102512e+18 FLOP
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
train and eval code for Delta RNN: https://github.com/IDSIA/recurrent-fwp/tree/master/language_modeling apache 2 license: https://github.com/IDSIA/recurrent-fwp/blob/master/language_modeling/LICENSE
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 77
- Benchmark data
- Delta RNN (+ full context)
Sources
Where this record came from and when it was last checked.
- Reference
- Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
- Last updated
- 11 February 2026
What the numbers mean
About this model
Delta RNN (+ full context) was published by IDSIA,SUPSI,King Abdullah University of Science and Technology (KAUST), in the country recorded as Switzerland, during June 2021. The category the publisher falls under is academia,Academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 1.1 × 10¹⁸ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 103,000,000 tokens of text.
Answers
Delta RNN (+ full context) — common questions
Delta RNN (+ full context)— how many parameters does it have?
It has a parameter count of 44.6M. 44.6M. 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.
Delta RNN (+ full context)— who created it?
It was published by IDSIA,SUPSI,King Abdullah University of Science and Technology (KAUST), based in Switzerland, an organisation categorised as academia,Academia,Academia.
Delta RNN (+ full context)— when was it released?
It was published in June 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Delta RNN (+ full context)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. 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.
Delta RNN (+ full context)— how much compute was used to train it?
Training consumed around 1.1 × 10¹⁸ 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.
Delta RNN (+ full context)— 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.
Delta RNN (+ full context)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.