Cube-Space AutoEncoder
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
- MIT-IBM Watson AI Lab
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 27 April 2020
- Authors
- Masataro Asai, Christian Muise
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Search
- Task
- Visual puzzles
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.
- Training data
- 4,243,600,000 tokens
"Finally, we tested Mandrill 15-puzzle, a significantly more challenging 4x4 variant of the sliding tile puzzle (Figure 1). We trained the network with more hyperparameter tuning it- erations (300) and a larger training set (50000). We gener- ated l = 14, 21 instances (20 each) and ran the system (Ta- ble 2, bottom right). "
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 × 10¹⁷ FLOP
- How it was established
- Hardware
(1) * (4113 * 10**9) * (24 * 3600) * (0.3) = 106608960000000000 (num gpu) * (peak flops) * (time in seconds) * (assumed utilization rate) = "For each domain, we searched for 100 iterations (≈15min/iter, 24 hours total) on a Tesla K80" 4.113 TFLOPS from https://www.techpowerup.com/gpu-specs/tesla-k80.c2616
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 K80
- Chips used
- 1
- Chip-hours
- 24
- Wall-clock time
- 24 hours
- Power draw
- 337 W
"For each domain, we searched for 100 iterations (≈15min/iter, 24 hours total) on a Tesla K80"
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
- 56
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
- Last updated
- 28 November 2025
What the numbers mean
Background
Cube-Space AutoEncoder was published by MIT-IBM Watson AI Lab, in United States of America, in April 2020. The organisation is categorised as academia,Industry.
It works in Vision, Search, and is recorded as doing visual puzzles.
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 1.1 × 10¹⁷ FLOP of computation, on NVIDIA Tesla K80 — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 4,243,600,000 tokens of text.
Answers
Cube-Space AutoEncoder — common questions
How many parameters does Cube-Space AutoEncoder have?
No parameter count has been published for Cube-Space AutoEncoder, which is why no memory or speed figure appears on this page.
Who created Cube-Space AutoEncoder?
Cube-Space AutoEncoder was published by MIT-IBM Watson AI Lab, based in United States of America, categorised as academia,Industry.
When was Cube-Space AutoEncoder released?
Cube-Space AutoEncoder was published in April 2020. 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 Cube-Space AutoEncoder used for?
Cube-Space AutoEncoder works in Vision, Search, and is recorded as handling visual puzzles. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Cube-Space AutoEncoder?
Around 1.1 × 10¹⁷ FLOP, on NVIDIA Tesla K80. 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 Cube-Space AutoEncoder?
None. Cube-Space AutoEncoder 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 Cube-Space AutoEncoder open source?
The licensing for Cube-Space AutoEncoder was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.