SqueezeBERT
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
- University of California (UC) Berkeley
- Organisation type
- Academia
- Country
- United States of America
- Published
- 10 June 2020
- Authors
- Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text autocompletion
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
- 51.1M
- Training data
- tokens
Rados
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 140
Sources
Where this record came from and when it was last checked.
- Reference
- SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
SqueezeBERT was published by University of California (UC) Berkeley, in United States of America, in June 2020. It comes out of academia.
It works in Language, and is recorded as doing text autocompletion.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
SqueezeBERT — common questions
Is SqueezeBERT open source?
The licensing for SqueezeBERT was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does SqueezeBERT have?
SqueezeBERT has 51.1M parameters. Rados. 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.
Who created SqueezeBERT?
SqueezeBERT was published by University of California (UC) Berkeley, based in United States of America, categorised as academia.
When was SqueezeBERT released?
SqueezeBERT was published in June 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 SqueezeBERT used for?
SqueezeBERT works in Language, and is recorded as handling text autocompletion. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run SqueezeBERT?
None. SqueezeBERT 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.