Claude 2
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
- Anthropic
- Organisation type
- Industry
- Country
- United States of America
- Published
- 11 July 2023
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Chat, Language modeling/generation, Question answering
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
- 3.9 × 10²⁴ FLOP
- How it was established
- Benchmarks,Hardware
https://colab.research.google.com/drive/1MdPuhS4Emaf23VXYZ-ooExDW-5GXZkw0#scrollTo=Ds0Q5X8aMnOY
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Compute cost
- $4,902,644
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
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Historical significance
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Claude 2 was published by Anthropic, in United States of America, in July 2023. It comes out of industry.
It works in Language, and is recorded as doing language modeling, Chat, Language modeling/generation, Question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 3.9 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The reason it appears in this catalogue at all is historical significance.
Answers
Claude 2 — common questions
What GPU do I need to run Claude 2?
None. Claude 2 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 Claude 2 open source?
No. Claude 2 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Claude 2 have?
No parameter count has been published for Claude 2, which is why no memory or speed figure appears on this page.
Who created Claude 2?
Claude 2 was published by Anthropic, based in United States of America, categorised as industry.
When was Claude 2 released?
Claude 2 was published in July 2023. 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 Claude 2 used for?
Claude 2 works in Language, and is recorded as handling language modeling, Chat, Language modeling/generation, Question answering. 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 Claude 2?
Around 3.9 × 10²⁴ 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.
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.