DeepSeek-V4-Pro 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-V4-Pro?
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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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
- 24 April 2026
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
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
- 1.6T
- Training data
- tokens
1.6T total, 49B active
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
- 9.7 × 10²⁴ FLOP
6 * 49e9 active parameters * 33e12 tokens = 9.702E24 for pre-training
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)
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
- Discretionary
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- Last updated
- 8 June 2026
What the numbers mean
Hardware requirements in practice
DeepSeek-V4-Pro reaches a parameter count of 1.6T. 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.
What this model is
DeepSeek-V4-Pro was published by DeepSeek, in the country recorded as China, during April 2026. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
How it was trained
Training it took a computation budget of roughly 9.7 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It is tracked in the underlying dataset for one reason in particular: discretionary.
Step by step
How to choose a GPU for DeepSeek-V4-Pro
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
Start from what it actually needs, which is the requirement of DeepSeek-V4-Pro. Capacity is the gate — a card either holds it or it does not.
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02
Match the context to your actual use
Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting DeepSeek-V4-Pro.
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03
Choose how far you will compress it
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
Sort by speed
Sort by speed to see how cards rank for DeepSeek-V4-Pro. 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
Tight means it loads and works with no room to raise the context later, in the case of DeepSeek-V4-Pro. 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
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 DeepSeek-V4-Pro.
Answers
DeepSeek-V4-Pro — common questions
DeepSeek-V4-Pro— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
DeepSeek-V4-Pro— how much compute was used to train it?
Training consumed around 9.7 × 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.
DeepSeek-V4-Pro— can I run it if it does not fit in my GPU?
It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 592.9 GB. Every figure here assumes the whole model is resident on the card.
DeepSeek-V4-Pro— 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-V4-Pro— 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-V4-Pro— 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.
DeepSeek-V4-Pro— 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-V4-Pro— how many parameters does it have?
It has a parameter count of 1.6T. 1.6T total, 49B 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.
DeepSeek-V4-Pro— who created it?
It was published by DeepSeek, based in China, an organisation categorised as industry.
DeepSeek-V4-Pro— when was it released?
It was published in April 2026.
DeepSeek-V4-Pro— 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. 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.
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.