ALLaM adapted13B
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
- Saudi Data and Artificial Intelligence Authority
- Organisation type
- Industry,Government
- Country
- Saudi Arabia
- Published
- 21 May 2024
- Authors
- Saudi Data and Artificial Intelligence Authority
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Translation, Question answering
- Base model
- LLaMA-13B
- Numerical format
- BF16
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
- 5,200,000,000,000 tokens
- Epochs
- 1
3,431,217,579(4.3B) total documents, with a total of 4,587,781,981,546(4.5T) words, and 5.2T tokens.
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
- 1.7 × 10²³ FLOP
Finetuning compute: 6*13000000000*1200000000000=9.36e+22 Llama 13B training FLOP: 7.8e+22 Total: 1.716e+23
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
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
on IBM watsonx platform https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx
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
- Confident
- Citations
- 69
Sources
Where this record came from and when it was last checked.
- Reference
- ALLaM: Large Language Models for Arabic and English
- Last updated
- 25 May 2026
What the numbers mean
About this model
ALLaM adapted13B was published by Saudi Data and Artificial Intelligence Authority, in the country recorded as Saudi Arabia, during May 2024. It comes out of an organisation categorised as industry,Government.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Translation, Question answering.
Its starting point was an existing base model, LLaMA-13B. Most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 1.7 × 10²³ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 5,200,000,000,000 tokens of text.
Answers
ALLaM adapted13B — common questions
ALLaM adapted13B— how many parameters does it have?
It has a parameter count of 13B. 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.
ALLaM adapted13B— who created it?
It was published by Saudi Data and Artificial Intelligence Authority, based in Saudi Arabia, an organisation categorised as industry,Government.
ALLaM adapted13B— when was it released?
It was published in May 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.
ALLaM adapted13B— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Translation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
ALLaM adapted13B— how much compute was used to train it?
Training consumed around 1.7 × 10²³ FLOP, on hardware recorded as NVIDIA A100. 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.
ALLaM adapted13B— 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.
ALLaM adapted13B— is it open source?
No. Its weights have not been 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.