DeepSeek-V3.1 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
Which GPUs can run DeepSeek-V3.1?
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
- 21 August 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, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation
- 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
- 840,000,000,000 tokens
671B total, 37B active
"840B tokens continued pretraining for long context extension on top of V3" "The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens." 839B in total
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
- 3.6 × 10²⁴ FLOP
- How it was established
- Operation counting
- Fine-tuning compute
- 1.9 × 10²³ FLOP
This was based on V3 which was trained on 14.8T tokens for a total of 14.8T + 840B = 15.64T pretraining tokens. Adding the FLOPs from continued pretraining onto our estimate for V3, which is 3.4e24 FLOPs, we get 3.4e24 + 1.9e23 = 3.59e24
6 FLOP / parameter / token * 37* 10^9 active parameters * 839 * 10^9 tokens = 1.86258e+23 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
- deepseek-ai
MIT license https://huggingface.co/deepseek-ai/DeepSeek-V3.1
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing DeepSeek-V3.1: our first step toward the agent era!
- Last updated
- 20 January 2026
What the numbers mean
What you need to run it
At 671B parameters, DeepSeek-V3.1 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.
About this model
DeepSeek-V3.1 was published by DeepSeek, in China, in August 2025. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation.
It is derived from DeepSeek-V3 rather than trained from scratch, which is the usual way a specialised model is produced.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the deepseek-ai organisation on Hugging Face.
Training and provenance
The training run consumed about 3.6 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 840,000,000,000 tokens of text.
Step by step
How to choose a GPU for DeepSeek-V3.1
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
The table lists every card that can hold DeepSeek-V3.1. That figure, not the card's headline performance, is what decides whether it runs.
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02
Match the context to your actual use
The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context DeepSeek-V3.1 can slip off a card that handles short questions easily.
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03
Set a quality floor
The quantisation column varies by card, because a bigger card holds a more accurate copy of DeepSeek-V3.1. Set a floor to hold the comparison at one level.
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04
Sort by speed
Sort by speed to see how cards rank for DeepSeek-V3.1. It will not match a gaming ordering — generation is bound by memory bandwidth.
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05
Read the fit column last
Tight means DeepSeek-V3.1 loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
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06
Check the card from the other side
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond DeepSeek-V3.1.
Answers
DeepSeek-V3.1 — common questions
When was DeepSeek-V3.1 released?
DeepSeek-V3.1 was published in August 2025.
What is DeepSeek-V3.1 used for?
DeepSeek-V3.1 works in Language, and is recorded as handling language modeling/generation, Code generation, Quantitative reasoning, Question answering, Search, System control, Instruction interpretation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download DeepSeek-V3.1?
Its weights are published under the deepseek-ai organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
How much compute was used to train DeepSeek-V3.1?
Around 3.6 × 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.
Can I run DeepSeek-V3.1 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 DeepSeek-V3.1 is rarely worth using — the nearest miss we calculate is short by 103.0 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run DeepSeek-V3.1 faster?
Two cards buy memory rather than speed. That matters for DeepSeek-V3.1 only if one card cannot hold it — 0 can, so a second adds little.
Why does the quantisation differ between cards for DeepSeek-V3.1?
Because capacity varies, so does how hard DeepSeek-V3.1 has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these DeepSeek-V3.1 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 DeepSeek-V3.1 open source?
Its weights are published, so DeepSeek-V3.1 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 DeepSeek-V3.1 have?
DeepSeek-V3.1 has 671B parameters. 671B total, 37B active. 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 DeepSeek-V3.1?
DeepSeek-V3.1 was published by DeepSeek, based in China, categorised as industry.
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.