ALLaM 34B
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
- 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
- 34B
- 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
- 1.1 × 10²⁴ FLOP
6*34000000000*5200000000000=1.060800e+24
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
- Unreleased
- Training code
- Unreleased
34b is not in the HF repo yet https://huggingface.co/ALLaM-AI
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
- AI Models for Arabic and English
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ALLaM 34B was published by Saudi Data and Artificial Intelligence Authority, in Saudi Arabia, in May 2024. industry,Government is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling/generation, Translation, Question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took roughly 1.1 × 10²⁴ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 5,200,000,000,000 tokens of text.
Answers
ALLaM 34B — common questions
What is ALLaM 34B used for?
ALLaM 34B works in Language, and is recorded as handling language modeling/generation, Translation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train ALLaM 34B?
Around 1.1 × 10²⁴ FLOP, on 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.
What GPU do I need to run ALLaM 34B?
None. ALLaM 34B 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 ALLaM 34B open source?
No. ALLaM 34B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does ALLaM 34B have?
ALLaM 34B has 34B 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 ALLaM 34B?
ALLaM 34B was published by Saudi Data and Artificial Intelligence Authority, based in Saudi Arabia, categorised as industry,Government.
When was ALLaM 34B released?
ALLaM 34B 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.
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.