ENAS
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
- Google Brain,Carnegie Mellon University (CMU),Stanford University
- Organisation type
- Industry,Academia,Academia
- Country
- United States of America
- Published
- 9 February 2018
- Authors
- Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean
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
- 24M
- Training data
- 929,000 tokens
- Epochs
- 150
24M Table 1
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 × 10¹⁶ FLOP
- How it was established
- Operation counting
Training on PTB: 6 FLOP / token / parameter * 24000000 parameters * 929000 tokens * 150 epochs = 2.00664e+16 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 GeForce GTX 1080 Ti
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
code for PTB. Apache license: https://github.com/google-research/google-research/tree/master/enas_lm
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
- 3,004
- Benchmark data
- ENAS
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Neural Architecture Search via Parameter Sharing
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ENAS was published by Google Brain,Carnegie Mellon University (CMU),Stanford University, in United States of America, in February 2018. industry,Academia,Academia is the category the publisher falls under.
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.
What went into building it
Producing it required around 2 × 10¹⁶ FLOP of arithmetic, on NVIDIA GeForce GTX 1080 Ti, which is a statement about the training budget rather than about inference.
Around 929,000 tokens went into training it.
Answers
ENAS — common questions
When was ENAS released?
ENAS was published in February 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.
What is ENAS used for?
ENAS 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 ENAS?
Around 2 × 10¹⁶ 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 ENAS?
None. ENAS 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 ENAS open source?
No. ENAS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ENAS have?
ENAS has 24M parameters. 24M Table 1. 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 ENAS?
ENAS was published by Google Brain,Carnegie Mellon University (CMU),Stanford University, based in United States of America, categorised as industry,Academia,Academia.
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.