GPT-3.5 (davinci-002)
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
- 15 March 2022
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Reinforcement learning
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.
- Training data
- 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
- 2.6 × 10²⁴ FLOP
- How it was established
- Comparison with other models,Benchmarks
https://colab.research.google.com/drive/1QSxa8YCWjEBQU7mrXLhw6TP1VX5oqgdW#scrollTo=Gt6Z6oZ26clI
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 A100 SXM4 40 GB
- Compute cost
- $4,897,552
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
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Historical significance,Significant use,SOTA improvement,Training cost
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Last updated
- 16 February 2026
What the numbers mean
What this model is
GPT-3.5 (davinci-002) was published by OpenAI, in the country recorded as United States of America, during March 2022. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling.
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 2.6 × 10²⁴ FLOP, on hardware recorded as NVIDIA A100 SXM4 40 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It is tracked in the underlying dataset for one reason in particular: historical significance,Significant use,SOTA improvement,Training cost.
Answers
GPT-3.5 (davinci-002) — common questions
GPT-3.5 (davinci-002)— when was it released?
It was published in March 2022. 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-3.5 (davinci-002)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. 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-3.5 (davinci-002)— how much compute was used to train it?
Training consumed around 2.6 × 10²⁴ FLOP, on hardware recorded as NVIDIA A100 SXM4 40 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.
GPT-3.5 (davinci-002)— 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-3.5 (davinci-002)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
GPT-3.5 (davinci-002)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
GPT-3.5 (davinci-002)— who created it?
It was published by OpenAI, 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.