Sparse all-MLP
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
- Meta AI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 14 April 2022
- Authors
- Ping Yu, Mikel Artexte, Myle Ott, Sam Shleifer, Hongyu Gong, Ves Stoyanov, Xian Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Self-supervised learning
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
- 9.4B
- Training data
- 100,000,000,000 tokens
Table 2: "In Section 4.4, we run our large model (9.41B parameters)"
100B tokens (Table 2) so 75B words.
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 5.3 × 10²⁰ FLOP
- How it was established
- Hardware
112 hours on 32 V100 GPUs assumed 0.33 util rate 112 hours *3600 seconds / hour *0.33 utilization *32 gpus *125000000000000 FLOPs=532224000000000000000
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
- NVIDIA V100
- Wall-clock time
- 112 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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 16
"Through extensive evaluations on language modeling, we show that sMLP outperforms stateof-the-art sparse Transformer-based MoE models in terms of generalization and 2× improvement in training efficiency." not an absolute SOTA "Table 6. Zero-shot priming evaluation: we provide head-to-head comparison of our sMLP model with FLOPs-matched state-of-the-art sparse Transformers on six representative NLP tasks evaluated in GPT-3 in-context learning(Brown et al., 2020b)."
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Language Modeling with Sparse all-MLP
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Sparse all-MLP was published by Meta AI, in United States of America, in April 2022. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training run consumed about 5.3 × 10²⁰ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 100,000,000,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Sparse all-MLP — common questions
Is Sparse all-MLP open source?
No. Sparse all-MLP has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Sparse all-MLP have?
Sparse all-MLP has 9.4B parameters. Table 2: "In Section 4.4, we run our large model (9.41B 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.
Who created Sparse all-MLP?
Sparse all-MLP was published by Meta AI, based in United States of America, categorised as industry.
When was Sparse all-MLP released?
Sparse all-MLP was published in April 2022. 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 Sparse all-MLP used for?
Sparse all-MLP works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Sparse all-MLP?
Around 5.3 × 10²⁰ FLOP, on NVIDIA V100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Sparse all-MLP?
None. Sparse all-MLP 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.