Universal-1
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
- AssemblyAI
- Organisation type
- Industry
- Published
- 3 April 2024
- Authors
- Francis McCann (lead), Luka Chkhetiani (lead), Andrew Ehrenberg, Robert McHardy, Rami Botros, Yash Khare, Andrea Vanzo, Taufiquzzaman Peyash, Gabriel Oexle, Michael Liang, Ilya Sklyar, Ahmed Etefy, Daniel McCrystal, William Pipsico Ferreira, Ruben Bousbib, Ben Gotthold, Soheyl Bahadoori, Enver Fakhan, Rahul Bagai, Mez Rashid, James He, Takuya Yoshioka, Travis Kupsche, Domenic Donato, Marco Ramponi…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
- Base model
- Conformer
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
- 600M
- Training data
- tokens
"Training on more than 12.5 million hours of diverse multilingual audio data, 600M-parameter Conformer RNN-T based Universal-1 achieves remarkable robustness"
"Trained on over 12.5 million hours of multilingual audio data" + fine-tuning 150K hours "A batch size of 2048 is used."
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
- Hardware utilisation
- MFU 54.0%
"Pre-training was performed on a cluster of v5e TPU chips, operating with 54% Model Flops Utilization (MFU), thanks to our optimized JAX implementation."
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
- API access
- 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
- Robust and accurate multilingual speech-to-text
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Universal-1 was published by AssemblyAI, during April 2024. The category the publisher falls under is industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Its starting point was an existing base model, Conformer. Most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Universal-1 — common questions
Universal-1— 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.
Universal-1— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Universal-1— how many parameters does it have?
It has a parameter count of 600M. "Training on more than 12.5 million hours of diverse multilingual audio data, 600M-parameter Conformer RNN-T based Universal-1 achieves remarkable robustness". 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.
Universal-1— who created it?
It was published by AssemblyAI, an organisation categorised as industry.
Universal-1— when was it released?
It was published in April 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.
Universal-1— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.