Multi-cell LSTM
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 Hyderabad
- Organisation type
- Academia
- Country
- India
- Published
- 15 November 2018
- Authors
- Thomas Cherian, Akshay Badola, Vineet Padmanabhan
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
- 7.2M
- Training data
- 929,000 tokens
- Epochs
- 50
Based on the details in the paper, the number of parameters in the Multi-cell LSTM model can be calculated as follows: The model has 2 hidden LSTM layers, each with 1500 hidden units Each LSTM unit is a multi-cell LSTM with 10 memory cells per unit For a standard LSTM layer with n hidden units: W matrix: n x input_size U matrix: n x n 4 bias vectors of size n (for input, forget, cell, output gates) So for each multi-cell LSTM layer with 1500 units and 10 cells per unit: W matrix: 1500 x inp…
50 epochs (from Figure 4)
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 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 7200000 parameters * 929000 tokens * 50 epochs = 2.00664e+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
- SOTA improvement
- Record confidence
- Likely
- Citations
- 6
- Benchmark data
- Multi-cell LSTM
"The proposed multi-cell LSTM language models outperform the state-of-the-art results on well-known Penn Treebank (PTB) setup"
Sources
Where this record came from and when it was last checked.
- Reference
- Multi-cell LSTM Based Neural Language Model
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Multi-cell LSTM was published by University of Hyderabad, in India, in November 2018. The organisation is categorised as 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.
Training and provenance
Training it took roughly 2 × 10¹⁵ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 929,000 tokens of text.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Multi-cell LSTM — common questions
Who created Multi-cell LSTM?
Multi-cell LSTM was published by University of Hyderabad, based in India, categorised as academia.
When was Multi-cell LSTM released?
Multi-cell LSTM was published in November 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 Multi-cell LSTM used for?
Multi-cell LSTM 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.
How much compute was used to train Multi-cell LSTM?
Around 2 × 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.
What GPU do I need to run Multi-cell LSTM?
None. Multi-cell LSTM 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 Multi-cell LSTM open source?
No. Multi-cell LSTM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Multi-cell LSTM have?
Multi-cell LSTM has 7.2M parameters. Based on the details in the paper, the number of parameters in the Multi-cell LSTM model can be calculated as follows: The model has 2 hidden LSTM layers, each with 1500 hidden units Each LSTM unit is a multi-cell LSTM with 10 memory cells per unit For a standard LSTM layer with n hidden units: W matrix: n x input_size U matrix: n x n 4 bias vectors of size n (for input, forget, cell, output gates) So for each multi-cell LSTM layer with 1500 units and 10 cells per unit: W matrix: 1500 x input_size U matrix: 1500 x 1500 4 bias vectors of size 1500 Number of parameters is same as standard LSTM layer For the 2 hidden layers: Input size for Layer 1: embedding dimension (estimated 300 in paper) Input size for Layer 2: 1500 (output of layer 1) Total params = Layer 1: 1500 x (300 + 1500 + 4) = 2,706,000 Layer 2: 1500 x (1500 + 1500 + 4) = 4,506,000 Total Parameters = 2,706,000 + 4,506,000 = 7,212,000 So the total number of parameters for the Multi-cell LSTM model with 2 layers of 1500 units and 10 cells per unit is approximately 7.2 million. 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?
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