Devstral Small TPS calculator

Open weights Mistral AI,All Hands AI 24B parameters May 2025

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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · IQ4_XS · 9.9 tok/s

Fastest card

B200

141 tok/s · 180 GB

Which GPUs can run Devstral Small?

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.

241 cards match

Calculating
Needs Quantisation Fit
141 tok/s

85–226 · low confidence

B200 NVIDIA 180 GB 8,000 GB/s Jan 2024 26.4 GB Q8_0 Comfortable
141 tok/s

85–226 · low confidence

B300 NVIDIA 288 GB 8,000 GB/s Sep 2025 26.4 GB Q8_0 Comfortable
113 tok/s

68–180 · low confidence

Radeon Instinct MI350X AMD 288 GB 8,190 GB/s Jan 2025 26.4 GB Q8_0 Comfortable
113 tok/s

68–180 · low confidence

Radeon Instinct MI355X AMD 288 GB 8,190 GB/s Jan 2025 26.4 GB Q8_0 Comfortable
90.2 tok/s

54–144 · low confidence

Radeon Instinct MI300 AMD 128 GB 6,550 GB/s Jan 2023 26.4 GB Q8_0 Comfortable
86.3 tok/s

52–138 · low confidence

H200 NVL NVIDIA 141 GB 4,890 GB/s Nov 2024 26.4 GB Q8_0 Comfortable
86.3 tok/s

52–138 · low confidence

H200 SXM 141 GB NVIDIA 141 GB 4,890 GB/s Nov 2024 26.4 GB Q8_0 Comfortable
82.6 tok/s

50–132 · low confidence

Radeon Instinct MI325X AMD 256 GB 6,000 GB/s Oct 2024 26.4 GB Q8_0 Comfortable
73.3 tok/s

44–117 · low confidence

Radeon Instinct MI300A AMD 128 GB 5,325 GB/s Dec 2023 26.4 GB Q8_0 Comfortable
73.3 tok/s

44–117 · low confidence

Radeon Instinct MI300X AMD 192 GB 5,325 GB/s Dec 2023 26.4 GB Q8_0 Comfortable
73.3 tok/s

44–117 · low confidence

Radeon Instinct MI308X AMD 192 GB 5,325 GB/s Dec 2023 26.4 GB Q8_0 Comfortable
69.5 tok/s

42–111 · low confidence

H100 NVL 94 GB NVIDIA 94 GB 3,940 GB/s Mar 2023 26.4 GB Q8_0 Comfortable
59.3 tok/s

36–95 · low confidence

H100 PCIe 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 26.4 GB Q8_0 Comfortable
59.3 tok/s

36–95 · low confidence

H100 SXM5 80 GB NVIDIA 80 GB 3,360 GB/s Oct 2022 26.4 GB Q8_0 Comfortable
59.3 tok/s

36–95 · low confidence

H100 SXM5 94 GB NVIDIA 94 GB 3,360 GB/s Mar 2023 26.4 GB Q8_0 Comfortable
59.3 tok/s

36–95 · low confidence

H100 SXM5 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 26.4 GB Q8_0 Comfortable
59.3 tok/s

36–95 · low confidence

H800 SXM5 NVIDIA 80 GB 3,360 GB/s Mar 2023 26.4 GB Q8_0 Comfortable
49.0 tok/s

29–78 · low confidence

Tesla V100 SXM2 16 GB NVIDIA 16 GB 1,130 GB/s Nov 2019 13.8 GB IQ4_XS Tight
45.2 tok/s

27–72 · low confidence

Radeon Instinct MI250 AMD 128 GB 3,280 GB/s Nov 2021 26.4 GB Q8_0 Comfortable
45.2 tok/s

27–72 · low confidence

Radeon Instinct MI250X AMD 128 GB 3,280 GB/s Nov 2021 26.4 GB Q8_0 Comfortable
41.6 tok/s

25–67 · low confidence

GeForce RTX 5080 NVIDIA 16 GB 960 GB/s Jan 2025 13.8 GB IQ4_XS Tight
38.9 tok/s

23–62 · low confidence

Tesla V100 DGXS 16 GB NVIDIA 16 GB 897 GB/s Mar 2018 13.8 GB IQ4_XS Tight
38.9 tok/s

23–62 · low confidence

Tesla V100 PCIe 16 GB NVIDIA 16 GB 897 GB/s Jun 2017 13.8 GB IQ4_XS Tight
38.9 tok/s

23–62 · low confidence

Tesla V100 SXM2 16 GB NVIDIA 16 GB 897 GB/s Jun 2017 13.8 GB IQ4_XS Tight
38.8 tok/s

23–62 · low confidence

GeForce RTX 5070 Ti NVIDIA 16 GB 896 GB/s Feb 2025 13.8 GB IQ4_XS 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,All Hands AI
Organisation type
Industry,Industry
Country
France, United States of America
Published
21 May 2025

What it does

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

Domain
Language
Task
Code generation, Language modeling/generation, Instruction interpretation, System control
Base model
Mistral Small 3.1

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
24B

24B

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 (unrestricted)
Training code
Unreleased

Apache 2.0 https://huggingface.co/mistralai/Devstral-Small-2505

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 the best open-source model for coding agents.
Last updated
28 November 2025

The extremes

The ten fastest GPUs that run Devstral Small

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.

  1. 01 B300 288 GB · 8,000 GB/s · Q8_0 141 tok/s
  2. 02 B200 180 GB · 8,000 GB/s · Q8_0 141 tok/s
  3. 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 113 tok/s
  4. 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 113 tok/s
  5. 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 90.2 tok/s
  6. 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 86.3 tok/s
  7. 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 86.3 tok/s
  8. 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 82.6 tok/s
  9. 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 73.3 tok/s
  10. 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 73.3 tok/s

The smallest GPUs that still run Devstral Small

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

  1. 01 Ryzen AI Z2 Extreme GPU 16 GB · needs 13.8 GB · IQ4_XS · tight 8.7 tok/s
  2. 02 Radeon RX 7700 16 GB · needs 13.8 GB · IQ4_XS · tight 21.0 tok/s
  3. 03 Arc Pro B50 16 GB · needs 13.8 GB · IQ4_XS · tight 6.3 tok/s
  4. 04 RTX PRO 2000 Blackwell 16 GB · needs 13.8 GB · IQ4_XS · tight 12.5 tok/s
  5. 05 Ryzen Z2 Extreme GPU 16 GB · needs 13.8 GB · IQ4_XS · tight 4.3 tok/s
  6. 06 Radeon RX 9060 XT 16 GB 16 GB · needs 13.8 GB · IQ4_XS · tight 10.9 tok/s
  7. 07 GeForce RTX 5060 Ti 16 GB 16 GB · needs 13.8 GB · IQ4_XS · tight 19.4 tok/s
  8. 08 GeForce RTX 5080 Mobile 16 GB · needs 13.8 GB · IQ4_XS · tight 38.8 tok/s
  9. 09 Radeon RX 9070 16 GB · needs 13.8 GB · IQ4_XS · tight 21.8 tok/s
  10. 10 Radeon RX 9070 XT 16 GB · needs 13.8 GB · IQ4_XS · tight 21.8 tok/s

What the numbers mean

The hardware side

Minimum card

Xeon Phi 7120P

Memory needed

13.8 GB

Fastest

141 tok/s

With 24B parameters, Devstral Small lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

At the low end, a Xeon Phi 7120P handles it — 16 GB, at IQ4_XS, for about 9.9 tokens per second.

A B200 is the fastest we calculate for it: about 141 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

Devstral Small was published by Mistral AI,All Hands AI, in France, in May 2025. It comes out of industry,Industry.

It works in Language, and is recorded as doing code generation, Language modeling/generation, Instruction interpretation, System control.

Its starting point was Mistral Small 3.1 — most models at this scale are adapted from an existing base rather than built from nothing.

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.

What decides the speed

Half the cards that hold it manage more than 19.4 tokens per second, and 193 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.

Step by step

How to choose a GPU for Devstral Small

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

  1. 01

    Read the memory figure first

    Every card here has been checked against Devstral Small — around 13.8 GB at IQ4_XS. Capacity is the gate — a card either holds it or it does not.

  2. 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 Devstral Small.

  3. 03

    Choose how far you will compress it

    Compression is what makes Devstral Small fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.

  4. 04

    Compare tokens per second, not specifications

    Ranking by tokens per second for Devstral Small follows memory bandwidth, not core counts, which is why the B200 tops it at 141 tok/s.

  5. 05

    Read the fit column last

    The fit column separates cards that just manage Devstral Small from those with room to spare. Buy for the second if the context might grow.

  6. 06

    Check the card from the other side

    Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Devstral Small.

Answers

Devstral Small — common questions

01

Is Devstral Small open source?

Its weights are published, so Devstral Small 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.

02

How many parameters does Devstral Small have?

Devstral Small has 24B parameters. 24B. 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.

03

Who created Devstral Small?

Devstral Small was published by Mistral AI,All Hands AI, based in France, categorised as industry,Industry.

04

When was Devstral Small released?

Devstral Small was published in May 2025.

05

What is Devstral Small used for?

Devstral Small works in Language, and is recorded as handling code generation, Language modeling/generation, Instruction interpretation, System control. These are the areas it was designed around; they describe intent rather than a hard boundary.

06

Where can I download Devstral Small?

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.

07

Can I run Devstral Small 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 4.4 GB. Our figures for Devstral Small assume it is fully resident.

08

Would two GPUs run Devstral Small faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Devstral Small alone, the case for pairing is weak.

09

Why does the quantisation differ between cards for Devstral Small?

Each card is shown running the least-compressed copy it can hold, and Devstral Small appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

10

How accurate are these Devstral Small speed estimates?

These are estimates with real error bars. The fastest result here, 85–226 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

11

What GPU do I need to run Devstral Small?

The smallest card in our catalogue that holds Devstral Small is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at IQ4_XS using about 13.8 GB, and produces roughly 9.9 tokens per second. 241 cards in total can run it.

12

How fast is Devstral Small on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 141 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 193 of the cards that can run Devstral Small clear that.

13

How much VRAM does Devstral Small need?

About 13.8 GB at IQ4_XS 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.

14

Can I run Devstral Small on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at IQ4_XS, using about 13.8 GB and generating roughly 49.0 tokens per second — a tight fit.

15

Can I run Devstral Small on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q6_K, using about 20.8 GB and generating roughly 34.4 tokens per second — a tight fit.

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