π0.7 (pi-0.7)
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
- Physical Intelligence
- Organisation type
- Industry
- Country
- United States of America
- Published
- 16 April 2026
- Authors
- Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, Vedant Choudhary, Foster Collins, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Maitrayee Dhaka, Jared DiCarlo, Danny Driess, Michael Equi, Adnan Esmail, Yunhao Fang, Chelsea Finn, Catherine Glossop, Thomas Godden, Ivan Goryachev, Lachlan Groom, Haroun Hab…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics, Vision
- 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
- 5.3B
- Training data
- tokens
"nitialized from the Gemma3 4B-parameter VLM [106] [...] (including a 400M-parameter vision encoder), and a flow matching action expert with 860M parameters. The model has about 5B total parameters."
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
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
- π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- Last updated
- 13 May 2026
What the numbers mean
About this model
π0.7 (pi-0.7) was published by Physical Intelligence, in United States of America, in April 2026. industry is the category the publisher falls under.
It works in Robotics, Vision, and is recorded as doing robotic manipulation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
π0.7 (pi-0.7) — common questions
Who created π0.7 (pi-0.7)?
π0.7 (pi-0.7) was published by Physical Intelligence, based in United States of America, categorised as industry.
When was π0.7 (pi-0.7) released?
π0.7 (pi-0.7) was published in April 2026.
What is π0.7 (pi-0.7) used for?
π0.7 (pi-0.7) works in Robotics, Vision, 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 π0.7 (pi-0.7)?
None. π0.7 (pi-0.7) 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 π0.7 (pi-0.7) open source?
No. π0.7 (pi-0.7) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does π0.7 (pi-0.7) have?
π0.7 (pi-0.7) has 5.3B parameters. "nitialized from the Gemma3 4B-parameter VLM [106] [...] (including a 400M-parameter vision encoder), and a flow matching action expert with 860M parameters. The model has about 5B total 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.
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.