DL scaling LM
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
- Baidu
- Organisation type
- Industry
- Country
- China
- Published
- 1 December 2017
- Authors
- Joel Hestness, Sharan Narang, Newsha Ardalani, G. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Supervised
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
- 177M
- Training data
- 400,000,000 tokens
We train the models on shards ranging from 0.1% up to 40% of the Billion Word Dataset.
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning Scaling is Predictable, Empirically
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DL scaling LM was published by Baidu, in China, in December 2017. industry is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Around 400,000,000 tokens went into training it.
The reason it appears in this catalogue at all is historical significance.
Answers
DL scaling LM — common questions
What is DL scaling LM used for?
DL scaling LM works in Language, and is recorded as handling language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DL scaling LM?
None. DL scaling LM 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 DL scaling LM open source?
No. DL scaling LM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DL scaling LM have?
DL scaling LM has 177M 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 DL scaling LM?
DL scaling LM was published by Baidu, based in China, categorised as industry.
When was DL scaling LM released?
DL scaling LM was published in December 2017. 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.