Multi-Token Prediction 13B
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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 30 April 2024
- Authors
- Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Code generation
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
- 13B
- Training data
- tokens
13B (Figure 1)
209.7B (Table S13)
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
- 1.5 × 10²³ FLOP
- How it was established
- Hardware
"training all models reported in the paper required around 500K GPU hours of computation on hardware of type A100-80GB and H100." A100-80 GB peak FLOP/s [assumed fp16 precision]: 77970000000000 H100 peak FLOP/s [assumed SXM5 TensorCore]: 989000000000000 assuming 50/50 usage: (77970000000000+989000000000000)*0.5*500000hours*3600s*0.3=2.880819e+23 for ALL models in the paper assuming this model has taken around 16% of all used compute (https://docs.google.com/spreadsheets/d/1Yc-HAdYgn6e9SUIliMaQ…
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 A100 SXM4 80 GB,NVIDIA H100 SXM5 80GB
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.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Better & Faster Large Language Models via Multi-token Prediction
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Multi-Token Prediction 13B was published by Facebook AI Research, in United States of America, in April 2024. industry is the category the publisher falls under.
It works in Language, and is recorded as doing code generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took roughly 1.5 × 10²³ FLOP of computation, on NVIDIA A100 SXM4 80 GB,NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.
Answers
Multi-Token Prediction 13B — common questions
How much compute was used to train Multi-Token Prediction 13B?
Around 1.5 × 10²³ FLOP, on NVIDIA A100 SXM4 80 GB,NVIDIA H100 SXM5 80GB. 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 Multi-Token Prediction 13B?
None. Multi-Token Prediction 13B 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 Multi-Token Prediction 13B open source?
No. Multi-Token Prediction 13B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Multi-Token Prediction 13B have?
Multi-Token Prediction 13B has 13B parameters. 13B (Figure 1). 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 Multi-Token Prediction 13B?
Multi-Token Prediction 13B was published by Facebook AI Research, based in United States of America, categorised as industry.
When was Multi-Token Prediction 13B released?
Multi-Token Prediction 13B 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.
What is Multi-Token Prediction 13B used for?
Multi-Token Prediction 13B works in Language, and is recorded as handling code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.