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 the country recorded as China, during December 2017. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training consumed a corpus of around 400,000,000 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
DL scaling LM — common questions
DL scaling LM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
DL scaling LM— 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.
DL scaling LM— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DL scaling LM— how many parameters does it have?
It has a parameter count of 177M. 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.
DL scaling LM— who created it?
It was published by Baidu, based in China, an organisation categorised as industry.
DL scaling LM— when was it released?
It 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.