VD-LSTM+REAL Large
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
- Salesforce Research,Stanford University
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 4 November 2016
- Authors
- Hakan Inan, Khashayar Khosravi, 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
- 51M
- Training data
- 929,000 tokens
- Epochs
- 75
51M (Table 3)
75 epochs (Figure 2b)
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
- 2.1 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 51000000 parameters * 929000 tokens * 75 epochs = 2.132055e+16 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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 404
- Benchmark data
- VD-LSTM+REAL Large
"Our framework leads to state of the art performance on the Penn Treebank"
Sources
Where this record came from and when it was last checked.
- Reference
- Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
VD-LSTM+REAL Large was published by Salesforce Research,Stanford University, in the country recorded as United States of America, during November 2016. It comes out of an organisation categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Producing it required arithmetic totalling around 2.1 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 929,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
VD-LSTM+REAL Large — common questions
VD-LSTM+REAL Large— how much compute was used to train it?
Training consumed around 2.1 × 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.
VD-LSTM+REAL Large— 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.
VD-LSTM+REAL Large— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
VD-LSTM+REAL Large— how many parameters does it have?
It has a parameter count of 51M. 51M (Table 3). 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.
VD-LSTM+REAL Large— who created it?
It was published by Salesforce Research,Stanford University, based in United States of America, an organisation categorised as industry,Academia.
VD-LSTM+REAL Large— when was it released?
It was published in November 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.
VD-LSTM+REAL Large— 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.
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.