GPT-2 (117M, SLW 110K)
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
- 13 August 2021
- Authors
- Conglong Li, Minjia Zhang, Yuxiong He
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
- 117M
- Training data
- tokens
batch size 512, 300K total training steps (157B 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 × 10²⁰ FLOP
- How it was established
- Operation counting
6ND formula: 6*157000000000*117000000=1.10214e+20
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 V100
- Chips used
- 128
- Power draw
- 77.6 kW
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 technique but I don't see training code for this model: https://github.com/microsoft/DeepSpeed
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
- 55
- Benchmark data
- GPT-2 (117M, SLW 110K)
Sources
Where this record came from and when it was last checked.
- Reference
- Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training
- Last updated
- 25 May 2026
What the numbers mean
What this model is
GPT-2 (117M, SLW 110K) was published by Microsoft, in the country recorded as United States of America, during August 2021. The publishing organisation is categorised as 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.
How it was trained
Producing it required arithmetic totalling around 1.1 × 10²⁰ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
GPT-2 (117M, SLW 110K) — common questions
GPT-2 (117M, SLW 110K)— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
GPT-2 (117M, SLW 110K)— when was it released?
It was published in August 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.
GPT-2 (117M, SLW 110K)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
GPT-2 (117M, SLW 110K)— how much compute was used to train it?
Training consumed around 1.1 × 10²⁰ FLOP, on hardware recorded as NVIDIA V100. 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.
GPT-2 (117M, SLW 110K)— 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.
GPT-2 (117M, SLW 110K)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GPT-2 (117M, SLW 110K)— how many parameters does it have?
It has a parameter count of 117M. batch size 512, 300K total training steps (157B tokens). 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.