DeepSeek-V4-Flash TPS calculator

Open weights DeepSeek 284B parameters April 2026

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

9 of 818 cards that can run it

Smallest card that fits

H200 NVL

141 GB · Q3_K_M · 109 tok/s

Fastest card

B200

153 tok/s · 180 GB

Which GPUs can run DeepSeek-V4-Flash?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

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Card memory

Also show cards that cannot run it

9 cards match

Calculating

					Needs	Quantisation	Fit
153 tok/s 92–245 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	153.0 GB	Q4_K_M	Tight
109 tok/s 66–175 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	119.9 GB	Q3_K_M	Tight
109 tok/s 66–175 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	119.9 GB	Q3_K_M	Tight
96.3 tok/s 58–154 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	219.1 GB	Q6_K	Tight
79.4 tok/s 48–127 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	153.0 GB	Q4_K_M	Tight
79.4 tok/s 48–127 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	153.0 GB	Q4_K_M	Tight
76.9 tok/s 46–123 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	219.1 GB	Q6_K	Tight
76.9 tok/s 46–123 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	219.1 GB	Q6_K	Tight
56.3 tok/s 34–90 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	219.1 GB	Q6_K	Tight

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: 284B
Training data: 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.

Training compute: 2.5 × 10²⁴ 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)

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

The extremes

The ten fastest GPUs for DeepSeek-V4-Flash

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run DeepSeek-V4-Flash

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

The hardware side

Minimum card

H200 NVL

Memory needed

119.9 GB

Fastest

153 tok/s

At 284B parameters, DeepSeek-V4-Flash is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job — 9 of the cards we track can hold it on their own, and all of them are datacentre parts.

At the low end, a H200 NVL handles it — 141 GB, at Q3_K_M, for about 109 tokens per second.

At the other end, a B200 generates roughly 153 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

DeepSeek-V4-Flash was published by DeepSeek, in China, in April 2026. industry is the category the publisher falls under.

It works in Language, and is recorded as doing language modeling/generation, Question answering.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

Understanding the speeds

Across every card that can run it, the middle of the range is about 79.4 tokens per second, and 9 of them clear the ten tokens per second that roughly matches reading speed.

Mixture-of-experts routing is why the speeds here look high for the parameter count. Only a fraction is read per token, but the whole thing has to be loaded.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

What went into building it

The training run consumed about 2.5 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Its inclusion criterion is discretionary.

Step by step

How to choose a GPU for DeepSeek-V4-Flash

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

Look at what DeepSeek-V4-Flash actually needs — around 119.9 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 DeepSeek-V4-Flash stops fitting a card that seemed fine.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of DeepSeek-V4-Flash — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

The speed ordering for DeepSeek-V4-Flash is effectively an ordering by memory bandwidth, which is why the B200 tops it at 153 tok/s.
05

Check the fit verdict before buying

A tight fit runs DeepSeek-V4-Flash but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 DeepSeek-V4-Flash is settled.

Answers

DeepSeek-V4-Flash — common questions

Is DeepSeek-V4-Flash open source?

Its weights are published, so DeepSeek-V4-Flash 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-V4-Flash have?

DeepSeek-V4-Flash has 284B parameters. 284B total, 13B 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-V4-Flash?

DeepSeek-V4-Flash was published by DeepSeek, based in China, categorised as industry.

When was DeepSeek-V4-Flash released?

DeepSeek-V4-Flash was published in April 2026.

What is DeepSeek-V4-Flash used for?

DeepSeek-V4-Flash works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download DeepSeek-V4-Flash?

The weights for DeepSeek-V4-Flash are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train DeepSeek-V4-Flash?

Around 2.5 × 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-V4-Flash 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-V4-Flash is rarely worth using — the nearest miss we calculate is short by 37.8 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run DeepSeek-V4-Flash faster?

A second card roughly doubles the memory available but not the generation rate. With 9 cards already able to run DeepSeek-V4-Flash alone, the case for pairing is weak.

Why does the quantisation differ between cards for DeepSeek-V4-Flash?

Each card is shown running the least-compressed copy it can hold, and DeepSeek-V4-Flash appears at 3 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these DeepSeek-V4-Flash speed estimates?

These are estimates with real error bars. The fastest result here, 92–245 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run DeepSeek-V4-Flash?

The smallest card in our catalogue that holds DeepSeek-V4-Flash is the H200 NVL, with 141 GB of memory. It runs the model at Q3_K_M using about 119.9 GB, and produces roughly 109 tokens per second. 9 cards in total can run it.

How fast is DeepSeek-V4-Flash on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 153 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 9 of the cards that can run DeepSeek-V4-Flash clear that.

How much VRAM does DeepSeek-V4-Flash need?

About 119.9 GB at Q3_K_M compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Source

Original publication

Record last updated 8 June 2026

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.

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