AmoebaNet-A (F=448)
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 February 2018
- Authors
- Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
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
- 469M
- Training data
- 1,150,000 tokens
Table 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
- 3.9 × 10²⁰ FLOP
- How it was established
- Hardware
450 K40 GPUs for 20k models (approx. 7 days). (From Imagenet paper-data, Besiroglu et al., forthcoming)
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 Tesla K40s
- Chips used
- 450
- Chip-hours
- 75,600
- Wall-clock time
- 168 hours (7 days)
- Power draw
- 229.2 kW
- Compute cost
- $11,766
"Each experiment ran on 450 K40 GPUs for 20k models (approx. 7 days)."
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
has code, but looks like just a toy model: https://colab.research.google.com/github/google-research/google-research/blob/master/evolution/regularized_evolution_algorithm/regularized_evolution.ipynb
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
- 3,322
Sources
Where this record came from and when it was last checked.
- Reference
- Regularized Evolution for Image Classifier Architecture Search
- Last updated
- 25 May 2026
What the numbers mean
What this model is
AmoebaNet-A (F=448) was published by Google Brain, in United States of America, in February 2018. The organisation is categorised as industry.
It works in Vision, and is recorded as doing image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 3.9 × 10²⁰ FLOP of computation, on NVIDIA Tesla K40s — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 1,150,000 tokens.
Answers
AmoebaNet-A (F=448) — common questions
Who created AmoebaNet-A (F=448)?
AmoebaNet-A (F=448) was published by Google Brain, based in United States of America, categorised as industry.
When was AmoebaNet-A (F=448) released?
AmoebaNet-A (F=448) 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 AmoebaNet-A (F=448) used for?
AmoebaNet-A (F=448) works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train AmoebaNet-A (F=448)?
Around 3.9 × 10²⁰ FLOP, on NVIDIA Tesla K40s. 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 AmoebaNet-A (F=448)?
None. AmoebaNet-A (F=448) 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 AmoebaNet-A (F=448) open source?
No. AmoebaNet-A (F=448) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AmoebaNet-A (F=448) have?
AmoebaNet-A (F=448) has 469M parameters. 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.
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.