ISS
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
- Duke University,Microsoft
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 15 September 2017
- Authors
- Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, Hai Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Numerical format
- FP32
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
- 11.1M
- Training data
- 929,000 tokens
- Epochs
- 55
11.1M (Table 2)
"All models are trained from scratch for 55 epochs."
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
- 3.4 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 11100000 parameters * 929000 tokens * 55 epochs = 3.402927e+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
- Open source
code (apache): https://github.com/wenwei202/iss-rnns/tree/master/ptb
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
- 146
- Benchmark data
- ISS
"Moreover, ISS learning can find a smaller RHN model with width 726, meanwhile improve the state-of-the-art perplexity as shown by the second entry in Table 2."
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Intrinsic Sparse Structures within Long Short-Term Memory
- Last updated
- 28 November 2025
What the numbers mean
About this model
ISS was published by Duke University,Microsoft, in United States of America, in September 2017. It comes out of academia,Industry.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training run consumed about 3.4 × 10¹⁵ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 929,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
ISS — common questions
How many parameters does ISS have?
ISS has 11.1M parameters. 11.1M (Table 2). 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 ISS?
ISS was published by Duke University,Microsoft, based in United States of America, categorised as academia,Industry.
When was ISS released?
ISS was published in September 2017. 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 ISS used for?
ISS 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 ISS?
Around 3.4 × 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 ISS?
None. ISS 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 ISS open source?
No. ISS has not had its weights published, so it exists only as a service controlled by its owner.
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.