Pointer Sentinel-LSTM (medium)
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
- MetaMind Inc,Salesforce
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 26 September 2016
- Authors
- Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher
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
- 21M
- Training data
- 929,000 tokens
- Epochs
- 64
"We also halve the learning rate when validation perplexity is worse than the previous iteration, stopping training when validation perplexity fails to improve for three epochs or when 64 epochs are reached"
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
- 7.5 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 21000000 parameters * 929000 tokens * 64 epochs = 7.491456e+15 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
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Confident
- Citations
- 4,010
- Benchmark data
- Pointer Sentinel-LSTM (medium)
"Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM"
Sources
Where this record came from and when it was last checked.
- Reference
- Pointer Sentinel Mixture Models
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Pointer Sentinel-LSTM (medium) was published by MetaMind Inc,Salesforce, in United States of America, in September 2016. It comes out of industry,Industry.
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.
What went into building it
Training it took roughly 7.5 × 10¹⁵ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 929,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited,SOTA improvement.
Answers
Pointer Sentinel-LSTM (medium) — common questions
What GPU do I need to run Pointer Sentinel-LSTM (medium)?
None. Pointer Sentinel-LSTM (medium) 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 Pointer Sentinel-LSTM (medium) open source?
No. Pointer Sentinel-LSTM (medium) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Pointer Sentinel-LSTM (medium) have?
Pointer Sentinel-LSTM (medium) has 21M 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.
Who created Pointer Sentinel-LSTM (medium)?
Pointer Sentinel-LSTM (medium) was published by MetaMind Inc,Salesforce, based in United States of America, categorised as industry,Industry.
When was Pointer Sentinel-LSTM (medium) released?
Pointer Sentinel-LSTM (medium) was published in September 2016. 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 Pointer Sentinel-LSTM (medium) used for?
Pointer Sentinel-LSTM (medium) 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 Pointer Sentinel-LSTM (medium)?
Around 7.5 × 10¹⁵ 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.
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.