Mistral Small 3.1 TPS calculator

Open weights Mistral AI 24B parameters March 2025

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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · IQ4_XS · 9.9 tok/s

Fastest card

B200

141 tok/s · 180 GB

Which GPUs can run Mistral Small 3.1?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
141 tok/s 120–169	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	26.1 GB	Q8_0	Comfortable
141 tok/s 120–169	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	26.1 GB	Q8_0	Comfortable
113 tok/s 68–180 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	26.1 GB	Q8_0	Comfortable
113 tok/s 68–180 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	26.1 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	26.1 GB	Q8_0	Comfortable
86.3 tok/s 73–104	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	26.1 GB	Q8_0	Comfortable
86.3 tok/s 73–104	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	26.1 GB	Q8_0	Comfortable
82.6 tok/s 50–132 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	26.1 GB	Q8_0	Comfortable
73.3 tok/s 44–117 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	26.1 GB	Q8_0	Comfortable
73.3 tok/s 44–117 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	26.1 GB	Q8_0	Comfortable
73.3 tok/s 44–117 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	26.1 GB	Q8_0	Comfortable
69.5 tok/s 59–83	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	26.1 GB	Q8_0	Comfortable
59.3 tok/s 50–71	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	26.1 GB	Q8_0	Comfortable
59.3 tok/s 50–71	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	26.1 GB	Q8_0	Comfortable
59.3 tok/s 50–71	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	26.1 GB	Q8_0	Comfortable
59.3 tok/s 50–71	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	26.1 GB	Q8_0	Comfortable
59.3 tok/s 50–71	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	26.1 GB	Q8_0	Comfortable
49.0 tok/s 42–59	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.6 GB	IQ4_XS	Tight
45.2 tok/s 27–72 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	26.1 GB	Q8_0	Comfortable
45.2 tok/s 27–72 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	26.1 GB	Q8_0	Comfortable
41.6 tok/s 35–50	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.6 GB	IQ4_XS	Tight
38.9 tok/s 33–47	Tesla V100 DGXS 16 GB NVIDIA	16 GB	897 GB/s	Mar 2018	13.6 GB	IQ4_XS	Tight
38.9 tok/s 33–47	Tesla V100 PCIe 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	13.6 GB	IQ4_XS	Tight
38.9 tok/s 33–47	Tesla V100 SXM2 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	13.6 GB	IQ4_XS	Tight
38.8 tok/s 33–47	GeForce RTX 5070 Ti NVIDIA	16 GB	896 GB/s	Feb 2025	13.6 GB	IQ4_XS	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: Mistral AI
Organisation type: Industry
Country: France
Published: 17 March 2025

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Language, Vision, Multimodal
Task: Language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation, Visual question answering, Character recognition (OCR)
Base model: Mistral Small 3

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: 24B
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.

How it was established: Comparison with other models

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: mistralai

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: Mistral Small 3.1: the best model in its weight class.
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Mistral Small 3.1

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 Mistral Small 3.1

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 you need to run it

Minimum card

Xeon Phi 7120P

Memory needed

13.6 GB

Fastest

141 tok/s

With 24B parameters, Mistral Small 3.1 lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

The least hardware that works is a Xeon Phi 7120P. Its 16 GB is enough at IQ4_XS compression, giving roughly 9.9 tokens per second.

The quickest result comes from a B200 at around 141 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Mistral Small 3.1 was published by Mistral AI, in France, in March 2025. The organisation is categorised as industry.

It works in Language, Vision, Multimodal, and is recorded as doing language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation, Visual question answering, Character recognition (OCR).

It is derived from Mistral Small 3 rather than trained from scratch, which is the usual way a specialised model is produced.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the mistralai organisation on Hugging Face.

How fast it runs, and why

The median result is around 19.4 tokens per second; 192 cards produce text faster than most people read it.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for Mistral Small 3.1

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

01

Check what it needs before anything else

The table lists every card that can hold Mistral Small 3.1 — around 13.6 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Mistral Small 3.1.
03

Set a quality floor

Compression is what makes Mistral Small 3.1 fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Ranking by tokens per second for Mistral Small 3.1 follows memory bandwidth, not core counts, which is why the B200 tops it at 141 tok/s.
05

Read the fit column last

The fit column separates cards that just manage Mistral Small 3.1 from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Mistral Small 3.1 alone — a card is usually bought for more than one model.

Answers

Mistral Small 3.1 — common questions

How fast is Mistral Small 3.1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 141 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 192 of the cards that can run Mistral Small 3.1 clear that.

How much VRAM does Mistral Small 3.1 need?

About 13.6 GB at IQ4_XS 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.

Can I run Mistral Small 3.1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at IQ4_XS, using about 13.6 GB and generating roughly 49.0 tokens per second — a tight fit.

Can I run Mistral Small 3.1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q6_K, using about 20.6 GB and generating roughly 34.4 tokens per second — a tight fit.

Is Mistral Small 3.1 open source?

Its weights are published, so Mistral Small 3.1 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 Mistral Small 3.1 have?

Mistral Small 3.1 has 24B parameters. 24B. 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 Mistral Small 3.1?

Mistral Small 3.1 was published by Mistral AI, based in France, categorised as industry.

When was Mistral Small 3.1 released?

Mistral Small 3.1 was published in March 2025.

What is Mistral Small 3.1 used for?

Mistral Small 3.1 works in Language, Vision, Multimodal, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation, Visual question answering, Character recognition (OCR). 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.

Where can I download Mistral Small 3.1?

Its weights are published under the mistralai organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

Can I run Mistral Small 3.1 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 Mistral Small 3.1 is rarely worth using — the nearest miss we calculate is short by 4.2 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Mistral Small 3.1 faster?

Capacity adds across cards; throughput does not. Since 241 of the cards we track already hold Mistral Small 3.1 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Mistral Small 3.1?

Because capacity varies, so does how hard Mistral Small 3.1 has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Mistral Small 3.1 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 120–169 tok/s on the B200 rather than a single number.

What GPU do I need to run Mistral Small 3.1?

The smallest card in our catalogue that holds Mistral Small 3.1 is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at IQ4_XS using about 13.6 GB, and produces roughly 9.9 tokens per second. 241 cards in total can run it.

Source

Original publication

Record last updated 28 November 2025

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