MEB
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
- 4 September 2021
- Authors
- W Liu, Z Wang, X Liu, N Zeng, Y Liu, FE Alsaadi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Search, Language
- Task
- Search
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
- 135B
- Training data
- 500,000,000,000 tokens
See paper title
"MEB uses three years of search logs from Bing as training data." TODO convert
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
- Hosted access (no API)
- Training code
- Unreleased
"MEB is running in production for 100 percent of Bing searches, in all regions and languages. It is the largest universal model we’re serving at Microsoft"
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
- Significant use
- Record confidence
- Confident
- Citations
- 26
"MEB is running in production for 100 percent of Bing searches, in all regions and languages."
Sources
Where this record came from and when it was last checked.
- Reference
- Make Every feature Binary: A 135B parameter sparse neural network for massively improved search relevance
- Last updated
- 28 November 2025
What the numbers mean
Background
MEB was published by Microsoft, in the country recorded as United States of America, during September 2021. The publishing organisation is categorised as industry.
It works in the domain of Search, Language, and is recorded as performing the task of search.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It was trained on a corpus of about 500,000,000,000 tokens of text.
Its inclusion criterion: significant use.
Answers
MEB — common questions
MEB— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
MEB— when was it released?
It was published in September 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.
MEB— what is it used for?
It works in the domain of Search, Language, and is recorded as handling the task of search. These are the areas it was designed around; they describe intent rather than a hard boundary.
MEB— 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.
MEB— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
MEB— how many parameters does it have?
It has a parameter count of 135B. See paper title. 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.
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.