Llama 4 Behemoth (preview)
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
- 5 April 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Chat, Code generation, Visual question answering, Translation, Language modeling/generation, Quantitative reasoning, Question answering
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
- 2T
- Training data
- 30,000,000,000,000 tokens
"Llama 4 Behemoth, a 288 billion active parameter model with 16 experts that is our most powerful yet and among the world’s smartest LLMs."
"The overall data mixture for training consisted of more than 30 trillion tokens, which is more than double the Llama 3 pre-training mixture and includes diverse text, image, and video datasets."
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
- 5.2 × 10²⁵ FLOP
- How it was established
- Operation counting
Behemoth's training dataset is at least 30T tokens: https://ai.meta.com/blog/llama-4-multimodal-intelligence/ 6 FLOP / parameter / token * 288 * 10^9 activated parameters * 30 * 10^12 tokens = 5.184e+25 FLOP
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 H100 SXM5 80GB
- Chips used
- 32,000
- Power draw
- 43.9 MW
- Compute cost
- $44,588,964
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
"While we’re not yet releasing Llama 4 Behemoth as it is still training"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- The Llama 4 herd: The beginning of a new era of natively multimodal AI innovation
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Llama 4 Behemoth (preview) was published by Meta AI, in United States of America, in April 2025. industry is the category the publisher falls under.
It works in Multimodal, Language, Vision, and is recorded as doing chat, Code generation, Visual question answering, Translation, Language modeling/generation, Quantitative reasoning, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 5.2 × 10²⁵ FLOP, on NVIDIA H100 SXM5 80GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Around 30,000,000,000,000 tokens went into training it.
Its inclusion criterion is training cost.
Answers
Llama 4 Behemoth (preview) — common questions
How many parameters does Llama 4 Behemoth (preview) have?
Llama 4 Behemoth (preview) has 2T parameters. "Llama 4 Behemoth, a 288 billion active parameter model with 16 experts that is our most powerful yet and among the world’s smartest LLMs.". 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 Llama 4 Behemoth (preview)?
Llama 4 Behemoth (preview) was published by Meta AI, based in United States of America, categorised as industry.
When was Llama 4 Behemoth (preview) released?
Llama 4 Behemoth (preview) was published in April 2025.
What is Llama 4 Behemoth (preview) used for?
Llama 4 Behemoth (preview) works in Multimodal, Language, Vision, and is recorded as handling chat, Code generation, Visual question answering, Translation, Language modeling/generation, Quantitative reasoning, Question answering. 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.
How much compute was used to train Llama 4 Behemoth (preview)?
Around 5.2 × 10²⁵ FLOP, on 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 Llama 4 Behemoth (preview)?
None. Llama 4 Behemoth (preview) 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 Llama 4 Behemoth (preview) open source?
No. Llama 4 Behemoth (preview) 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.