R1 1776 TPS calculator
Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.
Calculated for this model
818 cards we hold specifications for
Which GPUs can run R1 1776?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
0 cards match
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No card in our catalogue can run this model with these settings. |
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Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.
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
- 18 February 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Chat
- Base model
- DeepSeek-R1
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
- 671B
- Training data
- tokens
671B params
40k multilingual prompts assuming ~1000 tokens per prompt -> 40M 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.
- How it was established
- Operation counting
- Fine-tuning compute
- 8.9 × 10¹⁸ FLOP
Deepseek R1 has 37B active parameters fine-tuning dataset is speculatively estimated to be 40M tokens (see dataset size notes) 6 FLOP / token / parameter * 37 * 10^9 paramters * 40*10^6 tokens = 8.88e+18 FLOP
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Unreleased
- Hugging Face
- perplexity-ai
MIT license https://huggingface.co/perplexity-ai/r1-1776
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Open-sourcing R1 1776
- Last updated
- 28 November 2025
What the numbers mean
Hardware requirements in practice
R1 1776 reaches a parameter count of 671B. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 0.
About this model
R1 1776 was published by Perplexity, in the country recorded as United States of America, during February 2025. 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, Question answering, Chat.
It builds on DeepSeek-R1. That is the usual way a specialised model is produced.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. On Hugging Face it is published under the organisation perplexity-ai.
Step by step
How to choose a GPU for R1 1776
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
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01
Read the memory figure first
The table lists every card able to hold R1 1776. No amount of processing power compensates for a card that cannot hold it.
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02
Set the context length you will work at
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect, because at long context a card that handles short questions easily can be dropped by R1 1776.
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03
Choose how far you will compress it
Compression is what makes a model fit smaller cards, at some cost in accuracy. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
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04
Compare tokens per second, not specifications
Sort by speed to see how cards rank for R1 1776. It will not match a gaming ordering, because generation is bound by memory bandwidth.
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05
Look at the headroom, not just the fit
The fit column separates cards that just manage it from those with room to spare, in the case of R1 1776. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
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06
Check the card from the other side
Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on R1 1776.
Answers
R1 1776 — common questions
R1 1776— can I run it if it does not fit in my GPU?
Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. The nearest miss we calculate falls short by 147.5 GB. Every figure here assumes the whole model is resident on the card.
R1 1776— would two GPUs run it faster?
A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 0. So a second card is rarely the answer here.
R1 1776— why does the quantisation differ between cards?
Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
R1 1776— how accurate are these speed estimates?
Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: the range beneath each figure. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
R1 1776— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
R1 1776— how many parameters does it have?
It has a parameter count of 671B. 671B params. 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.
R1 1776— who created it?
It was published by Perplexity, based in United States of America, an organisation categorised as industry.
R1 1776— when was it released?
It was published in February 2025.
R1 1776— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering, Chat. 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.
R1 1776— where can I download it?
Its weights are published on Hugging Face, under the organisation perplexity-ai. We do not host model files — this site calculates what hardware is needed to run them.
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.