SRU++ Large only 2 attention layers (k=5) (WT103)
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
- ASAPP
- Organisation type
- Industry
- Country
- United States of America
- Published
- 24 February 2021
- Authors
- Tao Lei
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
- 225M
- Training data
- tokens
- Epochs
- 34.08
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
- 3.6 × 10¹⁹ FLOP
- How it was established
- Hardware
11 V100 GPU days 11*24*60*60*125000000000000*0.3=3.564e+19
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA V100
- Chips used
- 8
- Wall-clock time
- 33 hours
- Power draw
- 4.9 kW
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
code with MIT license (no script for WT103) https://github.com/asappresearch/sru/blob/master/language_model/README.md
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 54
- Benchmark data
- SRU++ Large only 2 attention layers (k=5)
Sources
Where this record came from and when it was last checked.
- Reference
- When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
- Last updated
- 11 February 2026
What the numbers mean
Background
SRU++ Large only 2 attention layers (k=5) (WT103) was published by ASAPP, in the country recorded as United States of America, during February 2021. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took a computation budget of roughly 3.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
SRU++ Large only 2 attention layers (k=5) (WT103) — common questions
SRU++ Large only 2 attention layers (k=5) (WT103)— how many parameters does it have?
It has a parameter count of 225M. 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.
SRU++ Large only 2 attention layers (k=5) (WT103)— who created it?
It was published by ASAPP, based in United States of America, an organisation categorised as industry.
SRU++ Large only 2 attention layers (k=5) (WT103)— when was it released?
It was published in February 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SRU++ Large only 2 attention layers (k=5) (WT103)— 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.
SRU++ Large only 2 attention layers (k=5) (WT103)— how much compute was used to train it?
Training consumed around 3.6 × 10¹⁹ FLOP, on hardware recorded as NVIDIA V100. 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.
SRU++ Large only 2 attention layers (k=5) (WT103)— 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.
SRU++ Large only 2 attention layers (k=5) (WT103)— is it open source?
No. Its weights have not been 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.