Megatron-LM (1.2B)
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
- NVIDIA
- Organisation type
- Industry
- Country
- United States of America
- Published
- 17 September 2019
- Authors
- Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
- 1.2B
- Training data
- 157,000,000,000 tokens
Table 1 in https://arxiv.org/pdf/1909.08053
300,000 *512*1,024 = 1.57e11 tokens
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
- 1.1 × 10²¹ FLOP
- How it was established
- Operation counting
300,000*512*1,024 = 1.57e11 tokens 6*1.57e11*1.2e9 = 1.13e21 FLOPs
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 Tesla V100 SXM3 32 GB
- Chips used
- 1
- Power draw
- 338 W
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
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
- Highly cited
- Record confidence
- Confident
- Citations
- 2,766
Sources
Where this record came from and when it was last checked.
- Reference
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- Last updated
- 29 May 2026
What the numbers mean
What this model is
Megatron-LM (1.2B) was published by NVIDIA, in the country recorded as United States of America, during September 2019. 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/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required arithmetic totalling around 1.1 × 10²¹ FLOP, on hardware recorded as NVIDIA Tesla V100 SXM3 32 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 157,000,000,000 tokens of text.
The reason it appears in this catalogue at all: highly cited.
Answers
Megatron-LM (1.2B) — common questions
Megatron-LM (1.2B)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Megatron-LM (1.2B)— how many parameters does it have?
It has a parameter count of 1.2B. Table 1 in https://arxiv.org/pdf/1909.08053. 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 (1.2B)— who created it?
It was published by NVIDIA, based in United States of America, an organisation categorised as industry.
Megatron-LM (1.2B)— when was it released?
It was published in September 2019. 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 (1.2B)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation. 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 (1.2B)— how much compute was used to train it?
Training consumed around 1.1 × 10²¹ FLOP, on hardware recorded as NVIDIA Tesla V100 SXM3 32 GB. 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.
Megatron-LM (1.2B)— 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.