AlphaX-1
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
- Facebook AI Research,Brown University
- Organisation type
- Industry,Academia
- Country
- United States of America, France
- Published
- 2 October 2019
- Authors
- Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca1
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Neural architecture search for computer vision, Image classification, Object detection, Image captioning
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
- 5.4M
- Training data
- 61,280,000 tokens
Table 3: multiadds for AlphaX-1 579M, parameters 5.4M
Standard image net training size, not otherwise specified
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
- 8.9 × 10¹⁷ FLOP
" Our models for ImageNet use polynomial learning rate schedule, starting with 0.05 and decay through 200 epochs." 1280000 images * 200 epochs *3 forward-backward adjustment * 1158000000 forward FLOP =889344000000000000
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 (non-commercial)
code, no license specified: https://github.com/linnanwang/AlphaX-NASBench101 training: https://github.com/linnanwang/AlphaX-NASBench101/blob/master/net_training.py
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 100
"In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency"
Sources
Where this record came from and when it was last checked.
- Reference
- AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
- Last updated
- 25 May 2026
What the numbers mean
About this model
AlphaX-1 was published by Facebook AI Research,Brown University, in United States of America, in October 2019. It comes out of industry,Academia.
It works in Vision, and is recorded as doing neural architecture search for computer vision, Image classification, Object detection, Image captioning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 8.9 × 10¹⁷ FLOP of arithmetic, on NVIDIA GeForce GTX 1080 Ti, which is a statement about the training budget rather than about inference.
Around 61,280,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
AlphaX-1 — common questions
What GPU do I need to run AlphaX-1?
None. AlphaX-1 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 AlphaX-1 open source?
No. AlphaX-1 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AlphaX-1 have?
AlphaX-1 has 5.4M parameters. Table 3: multiadds for AlphaX-1 579M, parameters 5.4M. 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 AlphaX-1?
AlphaX-1 was published by Facebook AI Research,Brown University, based in United States of America, categorised as industry,Academia.
When was AlphaX-1 released?
AlphaX-1 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.
What is AlphaX-1 used for?
AlphaX-1 works in Vision, and is recorded as handling neural architecture search for computer vision, Image classification, Object detection, Image captioning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train AlphaX-1?
Around 8.9 × 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.
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.