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 United States of America, in November 2016. It comes out of industry,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
Producing it required around 2.1 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 929,000 tokens.
Its inclusion criterion is sOTA improvement.
Answers
VD-LSTM+REAL Large — common questions
How much compute was used to train VD-LSTM+REAL Large?
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.
What GPU do I need to run VD-LSTM+REAL Large?
None. VD-LSTM+REAL Large 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 VD-LSTM+REAL Large open source?
No. VD-LSTM+REAL Large has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does VD-LSTM+REAL Large have?
VD-LSTM+REAL Large has 51M parameters. 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.
Who created VD-LSTM+REAL Large?
VD-LSTM+REAL Large was published by Salesforce Research,Stanford University, based in United States of America, categorised as industry,Academia.
When was VD-LSTM+REAL Large released?
VD-LSTM+REAL Large 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.
What is VD-LSTM+REAL Large used for?
VD-LSTM+REAL Large works in Language, and is recorded as handling 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.