SRU++ Base
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
- 148M
- Training data
- tokens
- Epochs
- 25.56
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
- 2.6 × 10¹⁹ FLOP
- How it was established
- Hardware
8 V100 GPU days 8*24*60*60*125000000000000*0.3=2.592e+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
- 24 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++ Base
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
- 1 December 2025
What the numbers mean
About this model
SRU++ Base was published by ASAPP, in United States of America, in February 2021. industry is the category the publisher falls under.
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.
What went into building it
Producing it required around 2.6 × 10¹⁹ FLOP of arithmetic, on NVIDIA V100, which is a statement about the training budget rather than about inference.
Answers
SRU++ Base — common questions
How much compute was used to train SRU++ Base?
Around 2.6 × 10¹⁹ FLOP, on 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.
What GPU do I need to run SRU++ Base?
None. SRU++ Base 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 SRU++ Base open source?
No. SRU++ Base has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does SRU++ Base have?
SRU++ Base has 148M parameters. 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 SRU++ Base?
SRU++ Base was published by ASAPP, based in United States of America, categorised as industry.
When was SRU++ Base released?
SRU++ Base 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.
What is SRU++ Base used for?
SRU++ Base works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.