aLSTM(depth-2)+RecurrentPolicy (PTB)
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
- University of Manchester
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 22 May 2018
- Authors
- Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot
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
- 24M
- Training data
- tokens
- Epochs
- 180
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
- 2.4 × 10¹⁶ FLOP
6*24000000*929000*180=2.407968e16
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
BSD-3 license: https://github.com/flennerhag/alstm/tree/master/examples
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Benchmark data
- aLSTM(depth-2)+RecurrentPolicy (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- Breaking the Activation Function Bottleneck through Adaptive Parameterization
- Last updated
- 11 February 2026
What the numbers mean
About this model
aLSTM(depth-2)+RecurrentPolicy (PTB) was published by University of Manchester, in United Kingdom of Great Britain and Northern Ireland, in May 2018. It comes out of academia.
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.
How it was trained
Training it took roughly 2.4 × 10¹⁶ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Answers
aLSTM(depth-2)+RecurrentPolicy (PTB) — common questions
Who created aLSTM(depth-2)+RecurrentPolicy (PTB)?
aLSTM(depth-2)+RecurrentPolicy (PTB) was published by University of Manchester, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was aLSTM(depth-2)+RecurrentPolicy (PTB) released?
aLSTM(depth-2)+RecurrentPolicy (PTB) was published in May 2018. 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 aLSTM(depth-2)+RecurrentPolicy (PTB) used for?
aLSTM(depth-2)+RecurrentPolicy (PTB) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train aLSTM(depth-2)+RecurrentPolicy (PTB)?
Around 2.4 × 10¹⁶ 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 aLSTM(depth-2)+RecurrentPolicy (PTB)?
None. aLSTM(depth-2)+RecurrentPolicy (PTB) 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 aLSTM(depth-2)+RecurrentPolicy (PTB) open source?
No. aLSTM(depth-2)+RecurrentPolicy (PTB) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does aLSTM(depth-2)+RecurrentPolicy (PTB) have?
aLSTM(depth-2)+RecurrentPolicy (PTB) has 24M parameters. 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.