AlphaChip
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
- 26 September 2024
- Authors
- Anna Goldie, Azalia Mirhoseini, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nova, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Chip design
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
- tokens
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.
- Training code
- Open source
Apache-2.0 license https://github.com/google-research/circuit_training
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- How AlphaChip transformed computer chip design
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
AlphaChip 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 Other, and is recorded as performing the task of chip design.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
AlphaChip — common questions
AlphaChip— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
AlphaChip— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
AlphaChip— 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.
AlphaChip— what is it used for?
It works in the domain of Other, and is recorded as handling the task of chip design. 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.
AlphaChip— 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.
AlphaChip— is it open source?
The licensing 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.