Megatron-LM (1T)
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 Research,NVIDIA,Stanford University
- Organisation type
- Industry,Industry,Academia
- Country
- United States of America
- Published
- 9 April 2021
- Authors
- Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text autocompletion
- 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
- 1T
- Training data
- tokens
[NOTE: They didn't train the model fully end-to-end, probably just to obtain enough information to gauge the ability to do model parallelisation] "Our approach allows us to perform training iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs with achieved per-GPU throughput of 52% of theoretical peak."
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 A100
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
- Open source
https://github.com/nvidia/megatron-lm?tab=readme-ov-file
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
- 1,172
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Megatron-LM (1T) was published by Microsoft Research,NVIDIA,Stanford University, in the country recorded as United States of America, during April 2021. The category the publisher falls under is industry,Industry,Academia.
It works in the domain of Language, and is recorded as performing the task of text autocompletion.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Megatron-LM (1T) — common questions
Megatron-LM (1T)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Megatron-LM (1T)— how many parameters does it have?
It has a parameter count of 1T. [NOTE: They didn't train the model fully end-to-end, probably just to obtain enough information to gauge the ability to do model parallelisation] "Our approach allows us to perform training iterations on a model with 1 trillion parameters at 502 petaFLOP/s on 3072 GPUs with achieved per-GPU throughput of 52% of theoretical peak.". 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.
Megatron-LM (1T)— who created it?
It was published by Microsoft Research,NVIDIA,Stanford University, based in United States of America, an organisation categorised as industry,Industry,Academia.
Megatron-LM (1T)— when was it released?
It was published in April 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.
Megatron-LM (1T)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of text autocompletion. 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.
Megatron-LM (1T)— 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.
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.