Mistral Large 2.1 TPS calculator

Open weights Mistral AI 123B parameters November 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 2.1?

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.

Search cards

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: 15 November 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, Question answering, Retrieval-augmented 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

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: Unknown

Sources

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

Reference: Our top-tier large model for high-complexity tasks with the lastest version released November 2024.
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Mistral Large 2.1

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 Large 2.1

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

The hardware side

Minimum card

RTX PRO 5000 72 GB Blackwell

Memory needed

60.8 GB

Fastest

31.3 tok/s

Mistral Large 2.1 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.

At the other end, a H100 NVL 94 GB generates roughly 31.3 tokens per second on it, on the strength of 3,940 GB/s of memory bandwidth.

About this model

Mistral Large 2.1 was published by Mistral AI, in France, in November 2024. It comes out of industry.

It works in Language, and is recorded as doing language modeling/generation, Translation, Code generation, Question answering, Retrieval-augmented generation.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the mistralai organisation on Hugging Face.

How fast it runs, and why

The median result is around 17.3 tokens per second; 36 cards produce text faster than most people read it.

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.

Step by step

How to choose a GPU for Mistral Large 2.1

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 Mistral Large 2.1 actually needs — around 60.8 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 Large 2.1.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Mistral Large 2.1 — 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

Ranking by tokens per second for Mistral Large 2.1 follows memory bandwidth, not core counts, which is why the H100 NVL 94 GB tops it at 31.3 tok/s.
05

Read the fit column last

A tight fit runs Mistral Large 2.1 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 Mistral Large 2.1 is settled.

Answers

Mistral Large 2.1 — common questions

How much VRAM does Mistral Large 2.1 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 2.1 open source?

Its weights are published, so Mistral Large 2.1 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 Large 2.1 have?

Mistral Large 2.1 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 2.1?

Mistral Large 2.1 was published by Mistral AI, based in France, categorised as industry.

When was Mistral Large 2.1 released?

Mistral Large 2.1 was published in November 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 2.1 used for?

Mistral Large 2.1 works in Language, and is recorded as handling language modeling/generation, Translation, Code generation, Question answering, Retrieval-augmented generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Mistral Large 2.1?

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 Large 2.1 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 Large 2.1 is rarely worth using — the nearest miss we calculate is short by 17.5 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Mistral Large 2.1 faster?

Capacity adds across cards; throughput does not. Since 38 of the cards we track already hold Mistral Large 2.1 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Mistral Large 2.1?

A larger card holds a more accurate copy. Across the cards that run Mistral Large 2.1, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Mistral Large 2.1 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 19–50 tok/s on the H100 NVL 94 GB, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run Mistral Large 2.1?

The smallest card in our catalogue that holds Mistral Large 2.1 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.

How fast is Mistral Large 2.1 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 2.1 clear that.

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