DeepSeekMath-V2 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 DeepSeekMath-V2?
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.
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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
- 27 November 2025
- Authors
- Zhihong Shao, Yuxiang Luo, Chengda Lu, Z. Z. Ren, Jiewen Hu, Tian Ye, Zhibin Gou, Shirong Ma, Xiaokang Zhang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Mathematical reasoning, Theorem proving, Proof generation, Proof verification
- Base model
- DeepSeek-V3.2-Exp-Base
- Numerical format
- FP8
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
- 685B
- Training data
- tokens
HF lists 685B params. Built on top of DeepSeek-V3.2-Exp-Base
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
Weights + model card: https://huggingface.co/deepseek-ai/DeepSeek-Math-V2 (Apache-2.0). Inference support referenced to https://github.com/deepseek-ai/DeepSeek-V3.2-Exp.
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
- SOTA improvement
- Record confidence
- Confident
Reported gold-level performance on IMO 2025/CMO 2024 and ~118/120 on Putnam 2024 with scaled test-time compute.
Sources
Where this record came from and when it was last checked.
- Reference
- DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning
- Last updated
- 15 January 2026
What the numbers mean
Hardware requirements in practice
At 685B parameters, DeepSeekMath-V2 is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 0 of the cards we track can hold it on their own, and all of them are datacentre parts.
Where it came from
DeepSeekMath-V2 was published by DeepSeek, in China, in November 2025. industry is the category the publisher falls under.
It works in Language, and is recorded as doing mathematical reasoning, Theorem proving, Proof generation, Proof verification.
Its starting point was DeepSeek-V3.2-Exp-Base — most models at this scale are adapted from an existing base rather than built from nothing.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
What went into building it
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Step by step
How to choose a GPU for DeepSeekMath-V2
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
Start from the memory column
Every card here has been checked against DeepSeekMath-V2. Capacity is the gate — a card either holds it or it does not.
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02
Set the context length you will work at
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason DeepSeekMath-V2 stops fitting a card that seemed fine.
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03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold. Setting a floor drops the cards that only manage DeepSeekMath-V2 by squeezing it further than you would want.
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04
Compare tokens per second, not specifications
The speed ordering for DeepSeekMath-V2 is effectively an ordering by memory bandwidth.
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05
Check the fit verdict before buying
The fit column separates cards that just manage DeepSeekMath-V2 from those with room to spare. Buy for the second if the context might grow.
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06
See what else that card runs
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond DeepSeekMath-V2.
Answers
DeepSeekMath-V2 — common questions
How accurate are these DeepSeekMath-V2 speed estimates?
Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as the range beneath each figure rather than a single number.
Is DeepSeekMath-V2 open source?
Its weights are published, so DeepSeekMath-V2 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.
How many parameters does DeepSeekMath-V2 have?
DeepSeekMath-V2 has 685B parameters. HF lists 685B params. Built on top of DeepSeek-V3.2-Exp-Base. 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 DeepSeekMath-V2?
DeepSeekMath-V2 was published by DeepSeek, based in China, categorised as industry.
When was DeepSeekMath-V2 released?
DeepSeekMath-V2 was published in November 2025.
What is DeepSeekMath-V2 used for?
DeepSeekMath-V2 works in Language, and is recorded as handling mathematical reasoning, Theorem proving, Proof generation, Proof verification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download DeepSeekMath-V2?
The weights for DeepSeekMath-V2 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
Can I run DeepSeekMath-V2 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 DeepSeekMath-V2 is rarely worth using — the nearest miss we calculate is short by 155.9 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run DeepSeekMath-V2 faster?
Capacity adds across cards; throughput does not. Since 0 of the cards we track already hold DeepSeekMath-V2 on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for DeepSeekMath-V2?
A larger card holds a more accurate copy. Across the cards that run DeepSeekMath-V2, 1 compression levels are used; the floor control above pins it to one.
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.