DeepSeek V4 Flash 0731 TPS calculator

Open weights DeepSeek 284B parameters July 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 cards that can run it

818 cards we hold specifications for

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 0731?

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: 31 July 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)
Hugging Face: deepseek-ai

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence: Likely

Sources

Where this record came from and when it was last checked.

Reference: DeepSeek-V4-Flash-0731 - HuggingFace
Last updated: 4 August 2026

The extremes

The ten fastest GPUs that run DeepSeek V4 Flash 0731

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 0731

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

What it takes to run this model

Minimum card

H200 NVL

Memory needed

119.9 GB

Fastest

153 tok/s

DeepSeek V4 Flash 0731 reaches a parameter count of 284B. 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: 9.

The smallest card that holds it is H200 NVL, with a memory capacity of 141 GB, running it at a compression of Q3_K_M and producing around 109 tokens per second.

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

Background

DeepSeek V4 Flash 0731 was published by DeepSeek, in the country recorded as China, during July 2026. 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.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation deepseek-ai.

Reading the throughput figures

The median result is around 79.4 tokens per second. Exceeding reading speed outright: 9 of them.

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.

How it was trained

Training it took a computation budget of roughly 2.5 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for DeepSeek V4 Flash 0731

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

The table lists every card able to hold DeepSeek V4 Flash 0731, needing around 119.9 GB at a compression of Q3_K_M. That figure, not the headline performance of a card, is what decides whether it runs.
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 V4 Flash 0731.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold, reaching a compression of Q3_K_M on the smallest card that fits. 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.
04

Compare tokens per second, not specifications

Ranking by tokens per second follows memory bandwidth rather than core counts, for DeepSeek V4 Flash 0731. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 153 tok/s.
05

Check the fit verdict before buying

Tight means it loads and works with no room to raise the context later, in the case of DeepSeek V4 Flash 0731. 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.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for DeepSeek V4 Flash 0731.

Answers

DeepSeek V4 Flash 0731 — common questions

DeepSeek V4 Flash 0731— 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 Flash 0731— how many parameters does it have?

It has a parameter count of 284B. 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.

DeepSeek V4 Flash 0731— who created it?

It was published by DeepSeek, based in China, an organisation categorised as industry.

DeepSeek V4 Flash 0731— when was it released?

It was published in July 2026.

DeepSeek V4 Flash 0731— 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. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

DeepSeek V4 Flash 0731— 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 V4 Flash 0731— how much compute was used to train it?

Training consumed 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.

DeepSeek V4 Flash 0731— 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 37.8 GB. Every figure here assumes the whole model is resident on the card.

DeepSeek V4 Flash 0731— 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: 9. So a second card is rarely the answer here.

DeepSeek V4 Flash 0731— 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: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

DeepSeek V4 Flash 0731— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 92–245 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

DeepSeek V4 Flash 0731— what GPU do I need to run it?

The smallest card in our catalogue that holds it is H200 NVL, with a memory capacity of 141 GB. It runs the model at a compression of Q3_K_M using about 119.9 GB, and produces roughly 109 tokens per second. The number of cards able to run it in total: 9.

DeepSeek V4 Flash 0731— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 9.

DeepSeek V4 Flash 0731— how much VRAM does it need?

It needs about 119.9 GB at a compression of Q3_K_M, 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 4 August 2026

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