GPT2-Large+LHOPT
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
- 2 June 2021
- Authors
- Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
- Base model
- GPT-2 (774M)
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
- 103,000,000 tokens
- Epochs
- 1
- Batch size
- 13,000
Figure 3
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
- 5 × 10²¹ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 4.7 × 10¹⁷ FLOP
base model compute (speculative confidence) 4.9536e+21 FLOP + fine-tune compute 4.6968e+17 FLOP = 4.9540697e+21 FLOP ________ estimation from the Arithmetic progress paper: 1.6E+21 FLOP
6 FLOP / parameter / token * 760000000 parameters * 103000000 tokens * 1 epoch = 4.6968e+17 FLOP
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
- Unreleased
there's a repo for the optimizer but no training code for this model: https://github.com/openai/LHOPT
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 35
- Benchmark data
- GPT2-Large+LHOPT
Sources
Where this record came from and when it was last checked.
- Reference
- A Generalizable Approach to Learning Optimizers
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
GPT2-Large+LHOPT was published by OpenAI, in United States of America, in June 2021. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation.
Its starting point was GPT-2 (774M) — most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took roughly 5 × 10²¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 103,000,000 tokens of text.
Answers
GPT2-Large+LHOPT — common questions
What is GPT2-Large+LHOPT used for?
GPT2-Large+LHOPT works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train GPT2-Large+LHOPT?
Around 5 × 10²¹ FLOP. 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 GPT2-Large+LHOPT?
None. GPT2-Large+LHOPT 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.
Is GPT2-Large+LHOPT open source?
No. GPT2-Large+LHOPT has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does GPT2-Large+LHOPT have?
GPT2-Large+LHOPT has 760M parameters. 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 GPT2-Large+LHOPT?
GPT2-Large+LHOPT was published by OpenAI, based in United States of America, categorised as industry.
When was GPT2-Large+LHOPT released?
GPT2-Large+LHOPT was published in June 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.
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.