ALOHA Unleashed
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 DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 8 September 2024
- Authors
- Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Kamyar Ghasemipour, Chelsea Finn, Ayzaan Wahid
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation
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
- 217M
- Training data
- tokens
"the Base model consists of 217M learnable parameters"
"we collect over 26k episodes for 5 real tasks, on 10 different robots in 2 different buildings over the course of 8 months" "images resized to 256x256"
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
- 3.6 × 10²¹ FLOP
- How it was established
- Hardware
265 hours *3600 sec / hour *64 GPUs *197000000000000 FLOP / s *0.3 [assumed utilization] = 3.6084096e+21 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
- Google TPU v5e
- Chips used
- 64
- Wall-clock time
- 265 hours (11 days)
- Power draw
- 28.4 kW
"We train our models with JAX [51] using 64 TPUv5e chips with a data parallel mesh. We use a batch size of 256 and train for 2M steps (about 265 hours of training). "
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.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- ALOHA Unleashed: A Simple Recipe for Robot Dexterity
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ALOHA Unleashed was published by Google DeepMind, in the country recorded as United States of America, during September 2024. The publishing organisation is categorised as industry.
It works in the domain of Robotics, and is recorded as performing the task of robotic manipulation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 3.6 × 10²¹ FLOP, on hardware recorded as Google TPU v5e. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
ALOHA Unleashed — common questions
ALOHA Unleashed— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
ALOHA Unleashed— how many parameters does it have?
It has a parameter count of 217M. "the Base model consists of 217M learnable parameters". 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.
ALOHA Unleashed— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
ALOHA Unleashed— when was it released?
It was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ALOHA Unleashed— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of robotic manipulation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
ALOHA Unleashed— how much compute was used to train it?
Training consumed around 3.6 × 10²¹ FLOP, on hardware recorded as Google TPU v5e. 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.
ALOHA Unleashed— what GPU do I need to run it?
None. This 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.
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.