PAGnol-XL
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
- LightOn,Laboratoire de Physique de l'Ecole Normale (LPENS),INRIA
- Organisation type
- Industry,Academia,Academia
- Country
- France
- Published
- 16 October 2021
- Authors
- Julien Launay, E.L. Tommasone, Baptiste Pannier, François Boniface, Amélie Chatelain, Alessandro Cappelli, Iacopo Poli, Djamé Seddah
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Language modeling/generation, Translation
- 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
- 1.5B
- Training data
- tokens
Section 4.1: 32G tokens => 32e9*0.75 = 24e9 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
- 2.6 × 10²⁰ FLOP
- How it was established
- Reported
They report their compute directly. From section 8: "About 62k GPU-hours on the Jean Zay HPC Cluster." Jean Zay uses both A100 and V100 GPUs, and maybe other stuff as well? Note they explicitly call out V100 in their Appendix A. https://www.hpcwire.com/2021/11/17/frances-jean-zay-supercomputer-gets-ai-boost-from-hpe-nvidia/
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 Tesla V100 SXM2
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 10
Sources
Where this record came from and when it was last checked.
- Reference
- PAGnol: An Extra-Large French Generative Model
- Last updated
- 25 May 2026
What the numbers mean
Background
PAGnol-XL was published by LightOn,Laboratoire de Physique de l'Ecole Normale (LPENS),INRIA, in France, in October 2021. The organisation is categorised as industry,Academia,Academia.
It works in Language, and is recorded as doing language modeling, Language modeling/generation, Translation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 2.6 × 10²⁰ FLOP, on NVIDIA Tesla V100 SXM2. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
PAGnol-XL — common questions
When was PAGnol-XL released?
PAGnol-XL was published in October 2021. 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 PAGnol-XL used for?
PAGnol-XL works in Language, and is recorded as handling language modeling, Language modeling/generation, Translation. 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 PAGnol-XL?
Around 2.6 × 10²⁰ FLOP, on NVIDIA Tesla V100 SXM2. 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 PAGnol-XL?
None. PAGnol-XL 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 PAGnol-XL open source?
No. PAGnol-XL has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PAGnol-XL have?
PAGnol-XL has 1.5B 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 PAGnol-XL?
PAGnol-XL was published by LightOn,Laboratoire de Physique de l'Ecole Normale (LPENS),INRIA, based in France, categorised as industry,Academia,Academia.
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.