Mistral Small v24.09 TPS calculator

Open weights Mistral AI 22B parameters September 2024

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 · Q4_K_M · 10.2 tok/s

Fastest card

B200

154 tok/s · 180 GB

Which GPUs can run Mistral Small v24.09?

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.

Search cards

Card memory

Also show cards that cannot run it

241 cards match

Calculating

					Needs	Quantisation	Fit
154 tok/s 131–185	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	24.6 GB	Q8_0	Comfortable
154 tok/s 131–185	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	24.6 GB	Q8_0	Comfortable
123 tok/s 74–197 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	24.6 GB	Q8_0	Comfortable
123 tok/s 74–197 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	24.6 GB	Q8_0	Comfortable
98.4 tok/s 59–157 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	24.6 GB	Q8_0	Comfortable
94.1 tok/s 80–113	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	24.6 GB	Q8_0	Comfortable
94.1 tok/s 80–113	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	24.6 GB	Q8_0	Comfortable
90.1 tok/s 54–144 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	24.6 GB	Q8_0	Comfortable
80.0 tok/s 48–128 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	24.6 GB	Q8_0	Comfortable
80.0 tok/s 48–128 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	24.6 GB	Q8_0	Comfortable
80.0 tok/s 48–128 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	24.6 GB	Q8_0	Comfortable
75.9 tok/s 64–91	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	24.6 GB	Q8_0	Comfortable
64.7 tok/s 55–78	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	24.6 GB	Q8_0	Comfortable
64.7 tok/s 55–78	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	24.6 GB	Q8_0	Comfortable
64.7 tok/s 55–78	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	24.6 GB	Q8_0	Comfortable
64.7 tok/s 55–78	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	24.6 GB	Q8_0	Comfortable
64.7 tok/s 55–78	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	24.6 GB	Q8_0	Comfortable
50.2 tok/s 43–60	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	14.4 GB	Q4_K_M	Tight
49.3 tok/s 30–79 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	24.6 GB	Q8_0	Comfortable
49.3 tok/s 30–79 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	24.6 GB	Q8_0	Comfortable
42.7 tok/s 36–51	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	14.4 GB	Q4_K_M	Tight
41.0 tok/s 25–66 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	24.6 GB	Q8_0	Comfortable
40.2 tok/s 24–64 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	24.6 GB	Q8_0	Comfortable
39.9 tok/s 34–48	Tesla V100 DGXS 16 GB NVIDIA	16 GB	897 GB/s	Mar 2018	14.4 GB	Q4_K_M	Tight
39.9 tok/s 34–48	Tesla V100 PCIe 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	14.4 GB	Q4_K_M	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 September 2024
Authors: Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Diogo Costa, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bou…

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: 22B
Training data: tokens

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 (non-commercial)
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: AI in abundance
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Mistral Small v24.09

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 v24.09

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

Hardware requirements in practice

Minimum card

Xeon Phi 7120P

Memory needed

14.4 GB

Fastest

154 tok/s

With 22B parameters, Mistral Small v24.09 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 entry point is the Xeon Phi 7120P: 16 GB of memory, Q4_K_M compression, roughly 10.2 tokens per second.

Top of the range is the B200, at roughly 154 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

Mistral Small v24.09 was published by Mistral AI, in France, in September 2024. The organisation is categorised as industry.

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

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.

What decides the speed

Half the cards that hold it manage more than 19.9 tokens per second, and 194 exceed reading speed outright.

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 v24.09

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

Look at what Mistral Small v24.09 actually needs — around 14.4 GB at Q4_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Mistral Small v24.09 can slip off a card that handles short questions easily.
03

Set a quality floor

Compression is what makes Mistral Small v24.09 fit smaller cards, at some cost in accuracy — Q4_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

Ranking by tokens per second for Mistral Small v24.09 follows memory bandwidth, not core counts, which is why the B200 tops it at 154 tok/s.
05

Check the fit verdict before buying

A tight fit runs Mistral Small v24.09 but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

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 v24.09 alone — a card is usually bought for more than one model.

Answers

Mistral Small v24.09 — common questions

What is Mistral Small v24.09 used for?

Mistral Small v24.09 works in Language, and is recorded as handling 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.

Where can I download Mistral Small v24.09?

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 v24.09 if it does not fit in my GPU?

It can be split between the card and system memory, but Mistral Small v24.09 generates painfully slowly that way — the nearest miss we calculate is short by 3.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Mistral Small v24.09 faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Mistral Small v24.09 alone, the case for pairing is weak.

Why does the quantisation differ between cards for Mistral Small v24.09?

A larger card holds a more accurate copy. Across the cards that run Mistral Small v24.09, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Mistral Small v24.09 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 131–185 tok/s on the B200 rather than a single number.

What GPU do I need to run Mistral Small v24.09?

The smallest card in our catalogue that holds Mistral Small v24.09 is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q4_K_M using about 14.4 GB, and produces roughly 10.2 tokens per second. 241 cards in total can run it.

How fast is Mistral Small v24.09 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 154 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 194 of the cards that can run Mistral Small v24.09 clear that.

How much VRAM does Mistral Small v24.09 need?

About 14.4 GB at Q4_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.

Can I run Mistral Small v24.09 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q4_K_M, using about 14.4 GB and generating roughly 50.2 tokens per second — a tight fit.

Can I run Mistral Small v24.09 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q6_K, using about 19.5 GB and generating roughly 37.5 tokens per second — a tight fit.

Is Mistral Small v24.09 open source?

Its weights are published, so Mistral Small v24.09 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 v24.09 have?

Mistral Small v24.09 has 22B parameters. 22B. 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 v24.09?

Mistral Small v24.09 was published by Mistral AI, based in France, categorised as industry.

When was Mistral Small v24.09 released?

Mistral Small v24.09 was published in September 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

Source

Original publication

Record last updated 28 November 2025

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.

All AI models