PPLX-70B-Online
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
- Perplexity
- Organisation type
- Industry
- Country
- United States of America
- Published
- 29 November 2023
- Authors
- Lauren Yang, Kevin Hu, Aarash Heydari, Gradey Wang, Dmitry Pervukhin, Nikhil Thota, Alexandr Yarats, Max Morozov, Denis Yarats
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering, Chat, Language modeling/generation
- Base model
- Llama 2-70B
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
- 70B
- Training data
- tokens
70B
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
https://docs.perplexity.ai/home
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Significant use
- Record confidence
- Likely
Probably significant use: "Perplexity, which has just 41 employees and is based out of a shared working space in San Francisco, has 10 million monthly active users, an impressive number for a young start-up." However, this includes everyone who uses Perplexity's app which also uses third party models like GPT-4. https://www.nytimes.com/2024/02/01/technology/perplexity-search-ai-google.html
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing PPLX Online LLMs
- Last updated
- 28 November 2025
What the numbers mean
Background
PPLX-70B-Online was published by Perplexity, in United States of America, in November 2023. The organisation is categorised as industry.
It works in Language, and is recorded as doing question answering, Chat, Language modeling/generation.
It is derived from Llama 2-70B rather than trained from scratch, which is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It is tracked in the underlying dataset for one reason in particular: significant use.
Answers
PPLX-70B-Online — common questions
What GPU do I need to run PPLX-70B-Online?
None. PPLX-70B-Online 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 PPLX-70B-Online open source?
No. PPLX-70B-Online has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PPLX-70B-Online have?
PPLX-70B-Online has 70B parameters. 70B. 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 PPLX-70B-Online?
PPLX-70B-Online was published by Perplexity, based in United States of America, categorised as industry.
When was PPLX-70B-Online released?
PPLX-70B-Online was published in November 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 PPLX-70B-Online used for?
PPLX-70B-Online works in Language, and is recorded as handling question answering, Chat, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.