XY-LENTXL
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 26 October 2022
- Authors
- Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Representation learning, Language modeling
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
- 2B
- Training data
- 600,000,000,000 tokens
- Batch size
- 4,000,000
Figure 1
Table 9: 150k steps 4M batch tokens per task 150000 * 4* 10^6 = 600B tokens
Table 9
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
- 7.2 × 10²¹ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 2*10^9 parameters [figure 1] * 600 * 10^9 tokens [see dataset size notes] = 7.2e+21 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
- 512
- Power draw
- 717.0 kW
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.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
XY-LENTXL was published by Microsoft, in United States of America, in October 2022. The organisation is categorised as industry.
It works in Language, and is recorded as doing representation learning, Language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required around 7.2 × 10²¹ FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
The training set ran to roughly 600,000,000,000 tokens.
Answers
XY-LENTXL — common questions
How many parameters does XY-LENTXL have?
XY-LENTXL has 2B parameters. 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 XY-LENTXL?
XY-LENTXL was published by Microsoft, based in United States of America, categorised as industry.
When was XY-LENTXL released?
XY-LENTXL was published in October 2022. 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 XY-LENTXL used for?
XY-LENTXL works in Language, and is recorded as handling representation learning, Language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train XY-LENTXL?
Around 7.2 × 10²¹ 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 XY-LENTXL?
None. XY-LENTXL 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 XY-LENTXL open source?
No. XY-LENTXL 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.