MusicLM
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 26 January 2023
- Authors
- Andrea Agostinelli, Timo I. Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matt Sharifi, Neil Zeghidour, Christian Frank
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Audio
- Task
- Audio generation
- Base model
- W2v-BERT
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
- 860M
- Training data
- tokens
"We use decoder-only Transformers for modeling the semantic stage and the acoustic stages of AudioLM. The models share the same architecture, composed of 24 layers, 16 attention heads, an embedding dimension of 1024, feed-forward layers of dimensionality 4096, dropout of 0.1, and relative positional embeddings (Raffel et al., 2020), resulting in 430M parameters per stage." "stage" seems to mean semantic + acoustic, so 860M total
>280k hours
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.
- Foundation model
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 671
"We demonstrate that our method outperforms baselines on MusicCaps, a hand-curated, high-quality dataset of 5.5k music-text pairs prepared by musicians."
Sources
Where this record came from and when it was last checked.
- Reference
- MusicLM: Generating Music From Text
- Last updated
- 25 May 2026
What the numbers mean
What this model is
MusicLM was published by Google, in United States of America, in January 2023. It comes out of industry.
It works in Audio, and is recorded as doing audio generation.
It is derived from W2v-BERT rather than trained from scratch, which is the usual way a specialised model is produced.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
MusicLM — common questions
How many parameters does MusicLM have?
MusicLM has 860M parameters. "We use decoder-only Transformers for modeling the semantic stage and the acoustic stages of AudioLM. The models share the same architecture, composed of 24 layers, 16 attention heads, an embedding dimension of 1024, feed-forward layers of dimensionality 4096, dropout of 0.1, and relative positional embeddings (Raffel et al., 2020), resulting in 430M parameters per stage." "stage" seems to mean semantic + acoustic, so 860M total. 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 MusicLM?
MusicLM was published by Google, based in United States of America, categorised as industry.
When was MusicLM released?
MusicLM was published in January 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 MusicLM used for?
MusicLM works in Audio, and is recorded as handling audio generation. 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.
What GPU do I need to run MusicLM?
None. MusicLM 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 MusicLM open source?
No. MusicLM has not had its weights published, so it exists only as a service controlled by its owner.
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.