Mistral Large 2 TPS calculator

Open weights Mistral AI 123B parameters July 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

38 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX PRO 5000 72 GB Blackwell

72 GB · Q3_K_M · 12.5 tok/s

Fastest card

H100 NVL 94 GB

31.3 tok/s · 94 GB

Which GPUs can run Mistral Large 2?

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

38 cards match

Calculating

					Needs	Quantisation	Fit
31.3 tok/s 19–50 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	75.1 GB	Q4_K_M	Tight
28.4 tok/s 17–45 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	68.0 GB	IQ4_XS	Tight
28.4 tok/s 17–45 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	68.0 GB	IQ4_XS	Tight
27.6 tok/s 17–44 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	132.4 GB	Q8_0	Comfortable
27.6 tok/s 17–44 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	132.4 GB	Q8_0	Comfortable
26.7 tok/s 16–43 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.1 GB	Q4_K_M	Tight
26.7 tok/s 16–43 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	75.1 GB	Q4_K_M	Tight
26.7 tok/s 16–43 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	75.1 GB	Q4_K_M	Tight
25.6 tok/s 15–41 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	103.8 GB	Q6_K	Tight
24.5 tok/s 15–39 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	103.8 GB	Q6_K	Comfortable
24.5 tok/s 15–39 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	103.8 GB	Q6_K	Comfortable
22.0 tok/s 13–35 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	132.4 GB	Q8_0	Comfortable
22.0 tok/s 13–35 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	132.4 GB	Q8_0	Comfortable
20.8 tok/s 12–33 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	103.8 GB	Q6_K	Tight
17.3 tok/s 10–28 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	68.0 GB	IQ4_XS	Tight
17.3 tok/s 10–28 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	68.0 GB	IQ4_XS	Tight
17.3 tok/s 10–28 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	68.0 GB	IQ4_XS	Tight
17.3 tok/s 10–28 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.0 GB	IQ4_XS	Tight
17.3 tok/s 10–28 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	68.0 GB	IQ4_XS	Tight
17.3 tok/s 10–28 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.0 GB	IQ4_XS	Tight
16.4 tok/s 10–26 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	68.0 GB	IQ4_XS	Tight
16.4 tok/s 10–26 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	68.0 GB	IQ4_XS	Tight
16.1 tok/s 10–26 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	132.4 GB	Q8_0	Comfortable
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	132.4 GB	Q8_0	Comfortable
14.3 tok/s 9–23 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	132.4 GB	Q8_0	Comfortable

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: 24 July 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, Translation, Code generation

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: 123B
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: Hardware,Cost,Benchmarks

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.

Likely above 10²³ FLOP: Yes
Why it is tracked: Training cost
Record confidence: Likely

Sources

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

Reference: Top-tier reasoning for high-complexity tasks, for your most sophisticated needs.
Last updated: 21 July 2026

The extremes

The ten fastest GPUs that run Mistral Large 2

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 Large 2

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

RTX PRO 5000 72 GB Blackwell

Memory needed

60.8 GB

Fastest

31.3 tok/s

Mistral Large 2 sits at 123B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 38 of the cards we track can hold it.

At the low end, a RTX PRO 5000 72 GB Blackwell handles it — 72 GB, at Q3_K_M, for about 12.5 tokens per second.

A H100 NVL 94 GB is the fastest we calculate for it: about 31.3 tokens per second, from 3,940 GB/s of memory bandwidth.

About this model

Mistral Large 2 was published by Mistral AI, in France, in July 2024. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation, Translation, Code generation.

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. It is published under the mistralai organisation on Hugging Face.

How fast it runs, and why

Half the cards that hold it manage more than 17.3 tokens per second, and 36 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

What went into building it

The reason it appears in this catalogue at all is training cost.

Step by step

How to choose a GPU for Mistral Large 2

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

Every card here has been checked against Mistral Large 2 — around 60.8 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 Mistral Large 2 stops fitting a card that seemed fine.
03

Set a quality floor

Compression is what makes Mistral Large 2 fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

The speed ordering for Mistral Large 2 is effectively an ordering by memory bandwidth, which is why the H100 NVL 94 GB tops it at 31.3 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Mistral Large 2 from those with room to spare. Buy for the second if the context might grow.
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 Mistral Large 2 is settled.

Answers

Mistral Large 2 — common questions

How fast is Mistral Large 2 on a GPU?

It depends on the card. The quickest we calculate is a H100 NVL 94 GB at about 31.3 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 36 of the cards that can run Mistral Large 2 clear that.

How much VRAM does Mistral Large 2 need?

About 60.8 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.

Is Mistral Large 2 open source?

Its weights are published, so Mistral Large 2 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 Large 2 have?

Mistral Large 2 has 123B parameters. 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 Large 2?

Mistral Large 2 was published by Mistral AI, based in France, categorised as industry.

When was Mistral Large 2 released?

Mistral Large 2 was published in July 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.

What is Mistral Large 2 used for?

Mistral Large 2 works in Language, and is recorded as handling language modeling/generation, Translation, Code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Mistral Large 2?

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 Large 2 if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 17.5 GB. Our figures for Mistral Large 2 assume it is fully resident.

Would two GPUs run Mistral Large 2 faster?

A second card roughly doubles the memory available but not the generation rate. With 38 cards already able to run Mistral Large 2 alone, the case for pairing is weak.

Why does the quantisation differ between cards for Mistral Large 2?

Each card is shown running the least-compressed copy it can hold, and Mistral Large 2 appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Mistral Large 2 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 19–50 tok/s on the H100 NVL 94 GB rather than a single number.

What GPU do I need to run Mistral Large 2?

The smallest card in our catalogue that holds Mistral Large 2 is the RTX PRO 5000 72 GB Blackwell, with 72 GB of memory. It runs the model at Q3_K_M using about 60.8 GB, and produces roughly 12.5 tokens per second. 38 cards in total can run it.

Source

Original publication

Record last updated 21 July 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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