Mistral Large 3 TPS calculator
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
818 cards we hold specifications for
Which GPUs can run Mistral Large 3?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
0 cards match
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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
- 2 December 2025
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
- 675B
- Training data
- tokens
"a sparse mixture-of-experts trained with 41B active and 675B total parameters."
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA H200
- Chips used
- 3,000
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)
- 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
- Introducing Mistral 3
- Last updated
- 11 August 2026
What the numbers mean
Hardware requirements in practice
Mistral Large 3 reaches a parameter count of 675B. That is beyond what any single graphics card holds. Running it means either splitting it across several cards or renting hardware built for the job, and every card able to hold it alone is a datacentre part. The number that can: 0.
What this model is
Mistral Large 3 was published by Mistral AI, in the country recorded as France, during December 2025. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of 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. On Hugging Face it is published under the organisation mistralai.
Step by step
How to choose a GPU for Mistral Large 3
The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.
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01
Start from the memory column
The table lists every card able to hold Mistral Large 3. That figure, not the headline performance of a card, is what decides whether it runs.
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02
Match the context to your actual use
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 3.
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03
Set a quality floor
Compression is what makes a model fit smaller cards, at some cost in accuracy. 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.
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04
Compare tokens per second, not specifications
Ranking by tokens per second follows memory bandwidth rather than core counts, for Mistral Large 3. It will not match a gaming ordering, because generation is bound by memory bandwidth.
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05
Read the fit column last
Tight means it loads and works with no room to raise the context later, in the case of Mistral Large 3. 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.
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06
Open the card you have settled on
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 Mistral Large 3.
Answers
Mistral Large 3 — common questions
Mistral Large 3— how many parameters does it have?
It has a parameter count of 675B. "a sparse mixture-of-experts trained with 41B active and 675B total 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.
Mistral Large 3— who created it?
It was published by Mistral AI, based in France, an organisation categorised as industry.
Mistral Large 3— when was it released?
It was published in December 2025.
Mistral Large 3— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Mistral Large 3— 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.
Mistral Large 3— 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 106.1 GB. Every figure here assumes the whole model is resident on the card.
Mistral Large 3— would two GPUs run it faster?
Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 0. So a second card is rarely the answer here.
Mistral Large 3— why does the quantisation differ between cards?
A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 1. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.
Mistral Large 3— how accurate are these speed estimates?
They are calculated from specifications rather than measured, and each carries a range. One example: the range beneath each figure. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
Mistral Large 3— 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.
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.