GPT-3 Large
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
- OpenAI
- Organisation type
- Industry
- Country
- United States of America
- Published
- 22 June 2020
- Authors
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, C…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text autocompletion, 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
- 760M
- Training data
- 300,000,000,000 tokens
- Batch size
- 500,000
760M
300b, per table d.1
0.5M, per table 2.1
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
- 1.4 × 10²¹ FLOP
- How it was established
- Reported
Table D.1 https://arxiv.org/abs/2005.14165
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 DGXS 32 GB
- Cloud vendor
- Microsoft
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
- 58,240
Sources
Where this record came from and when it was last checked.
- Reference
- Language Models are Few-Shot Learners
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
GPT-3 Large was published by OpenAI, in United States of America, in June 2020. industry is the category the publisher falls under.
It works in Language, and is recorded as doing text autocompletion, Language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 1.4 × 10²¹ FLOP, on NVIDIA Tesla V100 DGXS 32 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 300,000,000,000 tokens of text.
Answers
GPT-3 Large — common questions
Is GPT-3 Large open source?
The licensing for GPT-3 Large was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does GPT-3 Large have?
GPT-3 Large has 760M parameters. 760M. 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 GPT-3 Large?
GPT-3 Large was published by OpenAI, based in United States of America, categorised as industry.
When was GPT-3 Large released?
GPT-3 Large was published in June 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is GPT-3 Large used for?
GPT-3 Large works in Language, and is recorded as handling text autocompletion, 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.
How much compute was used to train GPT-3 Large?
Around 1.4 × 10²¹ FLOP, on NVIDIA Tesla V100 DGXS 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.
What GPU do I need to run GPT-3 Large?
None. GPT-3 Large 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.