DeepSeek-Prover-V2-671B 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 DeepSeek-Prover-V2-671B?
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
- DeepSeek
- Organisation type
- Industry
- Country
- China
- Published
- 30 April 2025
- Authors
- Z.Z. Ren, Zhihong Shao, Junxiao Song, Huajian Xin, Haocheng Wang, Wanjia Zhao, Liyue Zhang, Zhe Fu, Qihao Zhu, Dejian Yang, Z.F. Wu, Zhibin Gou, Shirong Ma, Hongxuan Tang, Yuxuan Liu, Wenjun Gao, Daya Guo, Chong Ruan
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, Quantitative reasoning, Mathematical reasoning
- Base model
- DeepSeek-V3
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
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 (restricted use)
- Training code
- Unreleased
- Hugging Face
- deepseek-ai
Deepseek Model License: Deepseek Model License: "DeepSeek reserves the right to restrict (remotely or otherwise) usage of the Model" https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-671B
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition
- Last updated
- 28 November 2025
What the numbers mean
What you need to run it
DeepSeek-Prover-V2-671B 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.
Background
DeepSeek-Prover-V2-671B was published by DeepSeek, in the country recorded as China, during April 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, Quantitative reasoning, Mathematical reasoning.
Rather than being trained from scratch, it is derived from DeepSeek-V3. Most models at this scale are adapted from an existing base rather than built from nothing.
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 deepseek-ai.
Step by step
How to choose a GPU for DeepSeek-Prover-V2-671B
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
Check what it needs before anything else
Every card here has been checked against DeepSeek-Prover-V2-671B. That figure, not the headline performance of a card, is what decides whether it runs.
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02
Decide how long your conversations run
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 DeepSeek-Prover-V2-671B.
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03
Set a quality floor
Each card runs the least-compressed copy it can hold. 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
Ranking by tokens per second follows memory bandwidth rather than core counts, for DeepSeek-Prover-V2-671B. It will not match a gaming ordering, because generation is bound by memory bandwidth.
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05
Read the fit column last
Tight means it loads and works with no room to raise the context later, in the case of DeepSeek-Prover-V2-671B. 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
Open the card you have settled on
Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond DeepSeek-Prover-V2-671B.
Answers
DeepSeek-Prover-V2-671B — common questions
DeepSeek-Prover-V2-671B— 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.
DeepSeek-Prover-V2-671B— how many parameters does it have?
It has a parameter count of 671B. 671B. 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.
DeepSeek-Prover-V2-671B— who created it?
It was published by DeepSeek, based in China, an organisation categorised as industry.
DeepSeek-Prover-V2-671B— when was it released?
It was published in April 2025.
DeepSeek-Prover-V2-671B— 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, Quantitative reasoning, Mathematical reasoning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DeepSeek-Prover-V2-671B— where can I download it?
Its weights are published on Hugging Face, under the organisation deepseek-ai. We do not host model files — this site calculates what hardware is needed to run them.
DeepSeek-Prover-V2-671B— 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.
DeepSeek-Prover-V2-671B— 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.
DeepSeek-Prover-V2-671B— why does the quantisation differ between cards?
A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
DeepSeek-Prover-V2-671B— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. 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.
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.