Mixtral 8x22B TPS calculator

Open weights Mistral AI 141B parameters April 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

37 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 SXM4 80 GB

80 GB · Q3_K_M · 59.8 tok/s

Fastest card

H100 NVL 94 GB

105 tok/s · 94 GB

Which GPUs can run Mixtral 8x22B?

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

37 cards match

Calculating

					Needs	Quantisation	Fit
105 tok/s 63–168 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	76.8 GB	IQ4_XS	Tight
99.1 tok/s 59–159 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	101.5 GB	Q5_K_M	Tight
98.5 tok/s 59–158 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	68.6 GB	Q3_K_M	Tight
98.5 tok/s 59–158 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	68.6 GB	Q3_K_M	Tight
89.6 tok/s 54–143 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	76.8 GB	IQ4_XS	Tight
86.9 tok/s 52–139 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	150.7 GB	Q8_0	Tight
86.9 tok/s 52–139 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	150.7 GB	Q8_0	Comfortable
84.2 tok/s 51–135 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	85.0 GB	Q4_K_M	Tight
84.2 tok/s 51–135 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	85.0 GB	Q4_K_M	Tight
80.6 tok/s 48–129 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	101.5 GB	Q5_K_M	Tight
77.2 tok/s 46–123 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	117.9 GB	Q6_K	Tight
77.2 tok/s 46–123 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	117.9 GB	Q6_K	Tight
69.4 tok/s 42–111 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	150.7 GB	Q8_0	Comfortable
69.4 tok/s 42–111 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	150.7 GB	Q8_0	Comfortable
59.8 tok/s 36–96 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	68.6 GB	Q3_K_M	Tight
59.8 tok/s 36–96 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	68.6 GB	Q3_K_M	Tight
59.8 tok/s 36–96 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	68.6 GB	Q3_K_M	Tight
59.8 tok/s 36–96 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.6 GB	Q3_K_M	Tight
59.8 tok/s 36–96 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	68.6 GB	Q3_K_M	Tight
59.8 tok/s 36–96 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.6 GB	Q3_K_M	Tight
56.9 tok/s 34–91 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	68.6 GB	Q3_K_M	Tight
56.9 tok/s 34–91 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	68.6 GB	Q3_K_M	Tight
50.8 tok/s 30–81 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	150.7 GB	Q8_0	Comfortable
49.6 tok/s 30–79 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	101.5 GB	Q5_K_M	Tight
49.6 tok/s 30–79 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	101.5 GB	Q5_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 April 2024
Authors: Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, L…

What it does

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

Domain: Language
Task: Language modeling/generation, Code generation, Translation, Quantitative reasoning, Question answering
Approach: Self-supervised learning

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: 141B
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.

Training compute: 2.3 × 10²⁴ FLOP
How it was established: Operation counting

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.

Likely above 10²³ FLOP: Yes
Record confidence: Speculative

Sources

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

Reference: Mixtral 8x22B
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Mixtral 8x22B

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 Mixtral 8x22B

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 it takes to run this model

Minimum card

A100 SXM4 80 GB

Memory needed

68.6 GB

Fastest

105 tok/s

Mixtral 8x22B reaches a parameter count of 141B. 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: 37.

The smallest card that holds it is A100 SXM4 80 GB, with a memory capacity of 80 GB, running it at a compression of Q3_K_M and producing around 59.8 tokens per second.

Top of the range is H100 NVL 94 GB, generating roughly 105 tokens per second on the strength of a memory bandwidth of 3,940 GB/s.

What this model is

Mixtral 8x22B was published by Mistral AI, in the country recorded as France, during April 2024. The category the publisher falls under is industry.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Code generation, Translation, Quantitative reasoning, Question answering.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation mistralai.

What decides the speed

Across every card that can run it, the middle of the range sits at 59.8 tokens per second. Producing text faster than most people read it: 35 of them.

Because it routes each token through a subset of its weights, it produces text at the pace of a much smaller model. The catch is memory: all of it still has to fit, so the speed is a bonus rather than a discount on hardware.

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.

How it was trained

The training run consumed about 2.3 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for Mixtral 8x22B

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 Mixtral 8x22B, needing around 68.6 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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, because at long context a card that handles short questions easily can be dropped by Mixtral 8x22B.
03

Set a quality floor

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

Sort by speed

Sort by speed to see how cards rank for Mixtral 8x22B. 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 105 tok/s.
05

Look at the headroom, not just the fit

Tight means it loads and works with no room to raise the context later, in the case of Mixtral 8x22B. 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

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for Mixtral 8x22B.

Answers

Mixtral 8x22B — common questions

Mixtral 8x22B— where can I download it?

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

Mixtral 8x22B— how much compute was used to train it?

Training consumed around 2.3 × 10²⁴ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Mixtral 8x22B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 20.2 GB. Every figure here assumes the whole model is resident on the card.

Mixtral 8x22B— 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: 37. So a second card is rarely the answer here.

Mixtral 8x22B— 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: 6. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Mixtral 8x22B— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 63–168 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.

Mixtral 8x22B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is A100 SXM4 80 GB, with a memory capacity of 80 GB. It runs the model at a compression of Q3_K_M using about 68.6 GB, and produces roughly 59.8 tokens per second. The number of cards able to run it in total: 37.

Mixtral 8x22B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is H100 NVL 94 GB, at about 105 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: 35.

Mixtral 8x22B— how much VRAM does it need?

It needs about 68.6 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.

Mixtral 8x22B— 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.

Mixtral 8x22B— how many parameters does it have?

It has a parameter count of 141B. 141B params, 39B active: https://mistral.ai/news/mixtral-8x22b/. 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.

Mixtral 8x22B— who created it?

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

Mixtral 8x22B— when was it released?

It was published in April 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.

Mixtral 8x22B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Code generation, Translation, Quantitative reasoning, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Source

Original publication

Record last updated 28 November 2025

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