LSTM (Hebbian, Cache, MbPA)
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
- DeepMind,University College London (UCL)
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 27 March 2018
- Authors
- Jack W Rae, Chris Dyer, Peter Dayan, Timothy P Lillicrap
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
- 530.4M
- Training data
- 175,181,505 tokens
- Epochs
- 80
- Batch size
- 51,200
Single layer LSTM with hidden dimension of 2048. Vocabulary for Gutenberg is 242,621; input and output embeddings are tied. Embedding layer (tied): 242,621 * 2048 = 496,887,808 LSTM layer: 4 * (2048 + 2048) * 2048 = 33,554,432 Total: 496,887,808 + 33,554,432 = 530,442,240
Omniglot: 32k images Wikitext-103: "Over 100 million tokens" Gutenberg: 175,181,505 tokens GigaWord v5: 4B tokens Gigaword is the largest dataset, but the largest training run uses Project Gutenberg.
Sequence length of 100, total of 512 batches. Batches are split between 8 GPUs.
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
- 3.3 × 10¹⁹ FLOP
- How it was established
- Hardware,Operation counting
They do training runs on a vision task and three language datasets. The largest dataset by size is GigaWord, but the largest training run is on the Gutenberg dataset, at 15B tokens. I assume the input embedding is done with an embedding lookup for efficiency rather than a dense matrix multiplication, so we only count FLOPs on the de-embedding. Ops counting: 6 * 15B * 530,442,240 = 4.774e19 Hardware: (8 * 1.87e13) * (6 * 24 * 3600) * 0.3 = 2.327e19 Geometric mean: sqrt(4.774e19 * 2.327e19) =…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA P100
- Chips used
- 8
- Wall-clock time
- 144 hours
- Power draw
- 4.2 kW
- Compute cost
- $591
6 days
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
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 47
- Benchmark data
- LSTM (Hebbian, Cache, MbPA)
Sources
Where this record came from and when it was last checked.
- Reference
- Fast Parametric Learning with Activation Memorization
- Last updated
- 11 February 2026
What the numbers mean
About this model
LSTM (Hebbian, Cache, MbPA) was published by DeepMind,University College London (UCL), in the country recorded as United Kingdom of Great Britain and Northern Ireland, during March 2018. The publishing organisation is categorised as industry,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.
How it was trained
Producing it required arithmetic totalling around 3.3 × 10¹⁹ FLOP, on hardware recorded as NVIDIA P100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 175,181,505 tokens of text.
Answers
LSTM (Hebbian, Cache, MbPA) — common questions
LSTM (Hebbian, Cache, MbPA)— 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.
LSTM (Hebbian, Cache, MbPA)— how much compute was used to train it?
Training consumed around 3.3 × 10¹⁹ FLOP, on hardware recorded as NVIDIA P100. 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.
LSTM (Hebbian, Cache, MbPA)— 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.
LSTM (Hebbian, Cache, MbPA)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
LSTM (Hebbian, Cache, MbPA)— how many parameters does it have?
It has a parameter count of 530.4M. Single layer LSTM with hidden dimension of 2048. Vocabulary for Gutenberg is 242,621; input and output embeddings are tied. Embedding layer (tied): 242,621 * 2048 = 496,887,808 LSTM layer: 4 * (2048 + 2048) * 2048 = 33,554,432 Total: 496,887,808 + 33,554,432 = 530,442,240. 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.
LSTM (Hebbian, Cache, MbPA)— who created it?
It was published by DeepMind,University College London (UCL), based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry,Academia.
LSTM (Hebbian, Cache, MbPA)— when was it released?
It was published in March 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.
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.