DARTS
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
- DeepMind,Carnegie Mellon University (CMU)
- Organisation type
- Industry,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 24 June 2018
- Authors
- Hanxiao Liu, Karen Simonyan, Yiming Yang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Neural Architecture Search - NAS
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
- 33M
- Training data
- 2,000,000 tokens
- Epochs
- 300
33M (Table 4) - parameters reported for PTB, but they say it is the same for WT-2 " WIKITEXT-2 We use embedding and hidden sizes 700, weight decay 5×10−7, and hidden-node variational dropout 0.15. Other hyperparameters remain the same as in our PTB experiments."
300 epochs for PTB (supposedly the same for WT-2
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
- 3.2 × 10¹⁷ FLOP
- How it was established
- Operation counting,Hardware
6 FLOP / parameter / token * 33000000 parameters * 2000000 tokens * 300 epochs = 1.188e+17 FLOP 11340000000000 FLOP / second / GPU * 1 GPU * 72 hours * 3600 sec / hour * 0.3 [assumed utilization] = 8.817984e+17 FLOP sqrt(1.188e+17*8.817984e+17) = 3.2366286e+17 FLOP 'Speculative' confidence since many variables are assumed based on PTB model training
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 GeForce GTX 1080 Ti
- Chips used
- 1
- Wall-clock time
- 72 hours
- Power draw
- 285 W
for PTB (they say WT-2 training is similar): The training takes 3 days on a single 1080Ti GPU with our PyTorch implementation 3*24 = 72 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
apache 2, training/test for wikitext: https://github.com/quark0/darts/tree/master/rnn
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 4,929
- Benchmark data
- DARTS
Sources
Where this record came from and when it was last checked.
- Reference
- DARTS: Differentiable Architecture Search
- Last updated
- 25 May 2026
What the numbers mean
About this model
DARTS was published by DeepMind,Carnegie Mellon University (CMU), in United Kingdom of Great Britain and Northern Ireland, in June 2018. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing language modeling, Neural Architecture Search - NAS.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 3.2 × 10¹⁷ FLOP, on NVIDIA GeForce GTX 1080 Ti. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 2,000,000 tokens of text.
Answers
DARTS — common questions
What is DARTS used for?
DARTS works in Language, and is recorded as handling language modeling, Neural Architecture Search - NAS. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train DARTS?
Around 3.2 × 10¹⁷ FLOP, on NVIDIA GeForce GTX 1080 Ti. 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 DARTS?
None. DARTS 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 DARTS open source?
No. DARTS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DARTS have?
DARTS has 33M parameters. 33M (Table 4) - parameters reported for PTB, but they say it is the same for WT-2 " WIKITEXT-2 We use embedding and hidden sizes 700, weight decay 5×10−7, and hidden-node variational dropout 0.15. Other hyperparameters remain the same as in our PTB experiments.". 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 DARTS?
DARTS was published by DeepMind,Carnegie Mellon University (CMU), based in United Kingdom of Great Britain and Northern Ireland, categorised as industry,Academia.
When was DARTS released?
DARTS was published in June 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.