Mistral Medium 3.5 TPS calculator

Open weights Mistral AI 128B parameters April 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

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 · 12.0 tok/s

Fastest card

H100 NVL 94 GB

30.1 tok/s · 94 GB

Which GPUs can run Mistral Medium 3.5?

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
30.1 tok/s 18–48 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	78.1 GB	Q4_K_M	Tight
27.3 tok/s 16–44 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	70.7 GB	IQ4_XS	Tight
27.3 tok/s 16–44 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	70.7 GB	IQ4_XS	Tight
26.5 tok/s 16–42 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	137.7 GB	Q8_0	Tight
26.5 tok/s 16–42 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	137.7 GB	Q8_0	Comfortable
25.7 tok/s 15–41 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	78.1 GB	Q4_K_M	Tight
25.7 tok/s 15–41 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	78.1 GB	Q4_K_M	Tight
25.7 tok/s 15–41 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	78.1 GB	Q4_K_M	Tight
24.6 tok/s 15–39 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	107.9 GB	Q6_K	Tight
23.5 tok/s 14–38 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	107.9 GB	Q6_K	Tight
23.5 tok/s 14–38 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	107.9 GB	Q6_K	Tight
21.1 tok/s 13–34 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	137.7 GB	Q8_0	Comfortable
21.1 tok/s 13–34 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	137.7 GB	Q8_0	Comfortable
20.0 tok/s 12–32 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	107.9 GB	Q6_K	Tight
16.6 tok/s 10–27 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	70.7 GB	IQ4_XS	Tight
16.6 tok/s 10–27 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	70.7 GB	IQ4_XS	Tight
16.6 tok/s 10–27 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	70.7 GB	IQ4_XS	Tight
16.6 tok/s 10–27 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	70.7 GB	IQ4_XS	Tight
16.6 tok/s 10–27 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	70.7 GB	IQ4_XS	Tight
16.6 tok/s 10–27 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	70.7 GB	IQ4_XS	Tight
15.8 tok/s 9–25 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	70.7 GB	IQ4_XS	Tight
15.8 tok/s 9–25 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	70.7 GB	IQ4_XS	Tight
15.5 tok/s 9–25 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	137.7 GB	Q8_0	Comfortable
13.7 tok/s 8–22 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	137.7 GB	Q8_0	Comfortable
13.7 tok/s 8–22 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	137.7 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: 29 April 2026

What it does

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

Domain: Language, Multimodal
Task: Question answering, Language modeling/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: 128B
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 (restricted use)

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 Medium 3.5 128B
Last updated: 27 May 2026

The extremes

The ten fastest GPUs that run Mistral Medium 3.5

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 Medium 3.5

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

RTX PRO 5000 72 GB Blackwell

Memory needed

63.2 GB

Fastest

30.1 tok/s

Mistral Medium 3.5 reaches a parameter count of 128B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 38.

The least hardware that works is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB, running it at a compression of Q3_K_M and producing around 12.0 tokens per second.

At the other end sits H100 NVL 94 GB, generating roughly 30.1 tokens per second on the strength of a memory bandwidth of 3,940 GB/s.

Background

Mistral Medium 3.5 was published by Mistral AI, in the country recorded as France, during April 2026. The category the publisher falls under is industry.

It works in the domain of Language, Multimodal, and is recorded as performing the task of question answering, Language modeling/generation.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Reading the throughput figures

The median result is around 16.6 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 36 of them.

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

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

Step by step

How to choose a GPU for Mistral Medium 3.5

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 Mistral Medium 3.5, needing around 63.2 GB at a compression of Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

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 Mistral Medium 3.5.
03

Decide how much compression you will accept

Compression is what makes a model fit smaller cards, at some cost in accuracy, 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

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Mistral Medium 3.5. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is H100 NVL 94 GB, at 30.1 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Mistral Medium 3.5. 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

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once you have settled on Mistral Medium 3.5.

Answers

Mistral Medium 3.5 — common questions

Mistral Medium 3.5— why does the quantisation differ between cards?

Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Mistral Medium 3.5— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 18–48 tok/s on H100 NVL 94 GB. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Mistral Medium 3.5— what GPU do I need to run it?

The smallest card in our catalogue that holds it is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB. It runs the model at a compression of Q3_K_M using about 63.2 GB, and produces roughly 12.0 tokens per second. The number of cards able to run it in total: 38.

Mistral Medium 3.5— how fast is it on a GPU?

It depends on the card. The quickest we calculate is H100 NVL 94 GB, at about 30.1 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: 36.

Mistral Medium 3.5— how much VRAM does it need?

It needs about 63.2 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.

Mistral Medium 3.5— 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.

Mistral Medium 3.5— how many parameters does it have?

It has a parameter count of 128B. 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.

Mistral Medium 3.5— who created it?

It was published by Mistral AI, based in France, an organisation categorised as industry.

Mistral Medium 3.5— when was it released?

It was published in April 2026.

Mistral Medium 3.5— what is it used for?

It works in the domain of Language, Multimodal, and is recorded as handling the task of question answering, Language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Mistral Medium 3.5— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Mistral Medium 3.5— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 20.5 GB. Every figure here assumes the whole model is resident on the card.

Mistral Medium 3.5— 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: 38. So a second card is rarely the answer here.

Source

Original publication

Record last updated 27 May 2026

The other direction

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