GPT-2 (1.5B, Curriculum Learning 45K)
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
- 1.5B
- Training data
- 157,000,000,000 tokens
- Epochs
- 1.6
- Batch size
- 4,000,000
Table 2 SLW 45K bsz4K-seqlen1K 58.8K 157B (1x) training time: 155Hr (2.2x) batch size 4000 sequence length 1000 58.8K steps 157B tokens 4*10^6*58800/157*10^9 = 1.6 epochs
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
- 2.4 × 10²¹ FLOP
- How it was established
- Comparison with other models,Hardware,Operation counting
"Experiments replicating GPT-2 models (117M and 1.5B) show that <..> our method reduces the required number of training tokens and wall clock time by up to 2.2x and 3.7x, respectively." GPT-2 estimated training compute: 1.9200000000009998e+21 FLOP (Speculative confidence) -> 1.9200000000009998e+21 / 3.7 = 5.1891892e+20 FLOP " All of the experiments are performed on 128 NVIDIA V100 GPUs (32GB memory). There are 16 nodes and 8 GPUs per node" 155 hours (Table 2) 125000000000000 FLOP/sec * 128 …
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
- Wall-clock time
- 155 hours
- 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
- Likely
- Citations
- 55
- Benchmark data
- GPT-2 (1.5B, Curriculum Learning 45K)
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 (1.5B, Curriculum Learning 45K) was published by Microsoft, in the country recorded as United States of America, during August 2021. 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.
How it was trained
Training it took a computation budget of roughly 2.4 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. 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.
Answers
GPT-2 (1.5B, Curriculum Learning 45K) — common questions
GPT-2 (1.5B, Curriculum Learning 45K)— 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 (1.5B, Curriculum Learning 45K)— 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.
GPT-2 (1.5B, Curriculum Learning 45K)— how much compute was used to train it?
Training consumed around 2.4 × 10²¹ 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 (1.5B, Curriculum Learning 45K)— 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 (1.5B, Curriculum Learning 45K)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GPT-2 (1.5B, Curriculum Learning 45K)— how many parameters does it have?
It has a parameter count of 1.5B. 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.
GPT-2 (1.5B, Curriculum Learning 45K)— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
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.