RT-2-X
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
- 13 October 2023
- Authors
- Open X-Embodiment Collaboration
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation
- Base model
- RT-2
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
- 55B
- Training data
- tokens
55B
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 951
SOTA is claimed on Google Robot - Custom/internal evaluation "Emergent skills evaluation. To investigate the transfer of knowledge across robots, we conduct experiments with the Google Robot, assessing the performance on tasks like the ones shown in Fig. 5. These tasks involve objects and skills that are not present in the RT-2 dataset but occur in the Bridge dataset [95] for a different robot (the WidowX robot). Results are shown in Table II, Emergent Skills Evaluation column. Comparing rows (…
Sources
Where this record came from and when it was last checked.
- Reference
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
RT-2-X was published by Google DeepMind, in United States of America, in October 2023. industry is the category the publisher falls under.
It works in Robotics, and is recorded as doing robotic manipulation.
Its starting point was RT-2 — most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Its inclusion criterion is sOTA improvement.
Answers
RT-2-X — common questions
Who created RT-2-X?
RT-2-X was published by Google DeepMind, based in United States of America, categorised as industry.
When was RT-2-X released?
RT-2-X was published in October 2023. 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 RT-2-X used for?
RT-2-X works in Robotics, and is recorded as handling robotic manipulation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run RT-2-X?
None. RT-2-X 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 RT-2-X open source?
No. RT-2-X has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does RT-2-X have?
RT-2-X has 55B parameters. 55B. 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.