RMSNorm (Transformer-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
- University of Edinburgh,University of Zurich
- Organisation type
- Academia,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, Switzerland
- Published
- 16 October 2019
- Authors
- Biao Zhang, Rico Sennrich
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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
- 65M
- Training data
- 7,500,000,000 tokens
Transformer-base has 65M parameters (Table 3 from https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)
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
- 2.8 × 10¹⁸ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP/parameter/token * 65000000 parameters * 7500000000 tokens = 2.925e18 FLOP 125000000000000 FLOP/GPU/sec * 19.25 hours * 3600 sec / hour * 1 GPUs * 0.3 [assumed utilization] = 2.59875e18 FLOP sqrt(2.925e18 * 2.59875e18) = 2.7570535e+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
- NVIDIA V100
- Chips used
- 1
- Wall-clock time
- 19 hours
- Power draw
- 338 W
Table 4: 231 seconds per 1k training steps 300k steps -> 300*231 seconds -> 19.25 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
- Open source
BSD-3 clause license https://github.com/bzhangGo/rmsnorm
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Root Mean Square Layer Normalization
- Last updated
- 11 February 2026
What the numbers mean
What this model is
RMSNorm (Transformer-base) was published by University of Edinburgh,University of Zurich, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during October 2019. The category the publisher falls under is academia,Academia.
It works in the domain of Language, and is recorded as performing the task of translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took a computation budget of roughly 2.8 × 10¹⁸ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 7,500,000,000 tokens of text.
Answers
RMSNorm (Transformer-base) — common questions
RMSNorm (Transformer-base)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RMSNorm (Transformer-base)— how many parameters does it have?
It has a parameter count of 65M. Transformer-base has 65M parameters (Table 3 from https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf). 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.
RMSNorm (Transformer-base)— who created it?
It was published by University of Edinburgh,University of Zurich, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia.
RMSNorm (Transformer-base)— when was it released?
It was published in October 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RMSNorm (Transformer-base)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
RMSNorm (Transformer-base)— how much compute was used to train it?
Training consumed around 2.8 × 10¹⁸ 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.
RMSNorm (Transformer-base)— 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.
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.