RFA-GATE-Gaussian-Stateful Big
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 Washington,DeepMind,Allen Institute for AI,Hebrew University of Jerusalem,The University of Hong Kong
- Organisation type
- Academia,Industry,Research collective,Academia,Academia
- Country
- United States of America, United Kingdom of Great Britain and Northern Ireland, Israel, Hong Kong
- Published
- 3 March 2021
- Authors
- Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A. Smith, Lingpeng Kong
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Translation
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
- 242M
- Training data
- 103,000,000 tokens
- Epochs
- 47.72
- Batch size
- 32,768
242m
"All models are trained for up to 150K gradient steps " batch size 64 "All models use a 512 block size during both training and evaluation, i.e., they read as input a segment of 512 consecutive tokens, without access to the context from previous mini-batches." 512*64*150000/103000000 = 47.72
512*64
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
- 7.1 × 10¹⁸ FLOP
- How it was established
- Operation counting
6 FLOP / token / parameter * 242000000 parameters * 512 tokens per sample * 64 samples per batch * 150000 steps = 7.1368704e+18 FLOP
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
- Google TPU v3
- Chips used
- 16
- Wall-clock time
- 3 hours
- Power draw
- 14.6 kW
7.1368704e+18 FLOP [estimated compute] / (123000000000000 FLOP / s * 16 GPUs * 3600 sec / hour * 0.3 [assumed precision]) = 3.36 hours
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.
- Record confidence
- Confident
- Citations
- 430
- Benchmark data
- RFA-GATE-Gaussian-Stateful Big
Sources
Where this record came from and when it was last checked.
- Reference
- Random Feature Attention
- Last updated
- 25 May 2026
What the numbers mean
What this model is
RFA-GATE-Gaussian-Stateful Big was published by University of Washington,DeepMind,Allen Institute for AI,Hebrew University of Jerusalem,The University of Hong Kong, in United States of America, in March 2021. academia,Industry,Research collective,Academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took roughly 7.1 × 10¹⁸ FLOP of computation, on Google TPU v3 — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 103,000,000 tokens.
Answers
RFA-GATE-Gaussian-Stateful Big — common questions
Who created RFA-GATE-Gaussian-Stateful Big?
RFA-GATE-Gaussian-Stateful Big was published by University of Washington,DeepMind,Allen Institute for AI,Hebrew University of Jerusalem,The University of Hong Kong, based in United States of America, categorised as academia,Industry,Research collective,Academia,Academia.
When was RFA-GATE-Gaussian-Stateful Big released?
RFA-GATE-Gaussian-Stateful Big was published in March 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 RFA-GATE-Gaussian-Stateful Big used for?
RFA-GATE-Gaussian-Stateful Big works in Language, and is recorded as handling language modeling/generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train RFA-GATE-Gaussian-Stateful Big?
Around 7.1 × 10¹⁸ FLOP, on Google TPU v3. 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 RFA-GATE-Gaussian-Stateful Big?
None. RFA-GATE-Gaussian-Stateful Big 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 RFA-GATE-Gaussian-Stateful Big open source?
No. RFA-GATE-Gaussian-Stateful Big has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does RFA-GATE-Gaussian-Stateful Big have?
RFA-GATE-Gaussian-Stateful Big has 242M parameters. 242m. 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?
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.