NAS with base 8 and shared embeddings
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 November 2016
- Authors
- Barret Zoph, Quoc V. Le
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
- 54M
- Training data
- 929,000 tokens
- Epochs
- 35
54M (Table 2)
"every child model is constructed and trained for 35 epochs"
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
- 1.1 × 10¹⁶ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 54000000 parameters * 929000 tokens * 35 epochs = 1.053486e+16 FLOP
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.
- Why it is tracked
- Highly cited
- Record confidence
- Confident
- Citations
- 5,894
- Benchmark data
- Neural Architecture Search with base 8 and shared embeddings
Sources
Where this record came from and when it was last checked.
- Reference
- Neural Architecture Search with Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
About this model
NAS with base 8 and shared embeddings was published by Google Brain, in United States of America, in November 2016. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling, Neural Architecture Search - NAS.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Producing it required around 1.1 × 10¹⁶ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 929,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
NAS with base 8 and shared embeddings — common questions
What is NAS with base 8 and shared embeddings used for?
NAS with base 8 and shared embeddings works in Language, and is recorded as handling language modeling, Neural Architecture Search - NAS. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train NAS with base 8 and shared embeddings?
Around 1.1 × 10¹⁶ FLOP. 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 NAS with base 8 and shared embeddings?
None. NAS with base 8 and shared embeddings 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 NAS with base 8 and shared embeddings open source?
No. NAS with base 8 and shared embeddings has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does NAS with base 8 and shared embeddings have?
NAS with base 8 and shared embeddings has 54M parameters. 54M (Table 2). 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 NAS with base 8 and shared embeddings?
NAS with base 8 and shared embeddings was published by Google Brain, based in United States of America, categorised as industry.
When was NAS with base 8 and shared embeddings released?
NAS with base 8 and shared embeddings was published in November 2016. 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.