Qwen2.5 Instruct (32B) TPS calculator

Open weights Alibaba 32.5B parameters September 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · Q3_K_M · 22.5 tok/s

Fastest card

B200

104 tok/s · 180 GB

Which GPUs can run Qwen2.5 Instruct (32B)?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
104 tok/s 89–125	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	35.5 GB	Q8_0	Comfortable
104 tok/s 89–125	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	35.5 GB	Q8_0	Comfortable
83.3 tok/s 50–133 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	35.5 GB	Q8_0	Comfortable
83.3 tok/s 50–133 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	35.5 GB	Q8_0	Comfortable
66.6 tok/s 40–107 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	35.5 GB	Q8_0	Comfortable
63.7 tok/s 54–76	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	35.5 GB	Q8_0	Comfortable
63.7 tok/s 54–76	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	35.5 GB	Q8_0	Comfortable
61.0 tok/s 37–98 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	35.5 GB	Q8_0	Comfortable
54.1 tok/s 32–87 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	35.5 GB	Q8_0	Comfortable
54.1 tok/s 32–87 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	35.5 GB	Q8_0	Comfortable
54.1 tok/s 32–87 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	35.5 GB	Q8_0	Comfortable
51.3 tok/s 44–62	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	35.5 GB	Q8_0	Comfortable
43.8 tok/s 37–53	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	35.5 GB	Q8_0	Comfortable
43.8 tok/s 37–53	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	35.5 GB	Q8_0	Comfortable
43.8 tok/s 37–53	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	35.5 GB	Q8_0	Comfortable
43.8 tok/s 37–53	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	35.5 GB	Q8_0	Comfortable
43.8 tok/s 37–53	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	35.5 GB	Q8_0	Comfortable
40.3 tok/s 34–48	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.3 GB	Q4_K_M	Tight
36.7 tok/s 31–44	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.3 GB	Q4_K_M	Tight
35.4 tok/s 30–42	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	27.9 GB	Q6_K	Tight
35.4 tok/s 30–42	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	27.9 GB	Q6_K	Tight
33.9 tok/s 29–41	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.9 GB	Q6_K	Tight
33.9 tok/s 29–41	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.9 GB	Q6_K	Tight
33.3 tok/s 20–53 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	35.5 GB	Q8_0	Comfortable
33.3 tok/s 20–53 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	35.5 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: Alibaba
Organisation type: Industry
Country: China
Published: 19 September 2024
Authors: Qwen Team

What it does

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

Domain: Language
Task: Code generation, Code autocompletion, Quantitative reasoning, Question answering, Language modeling/generation
Base model: Qwen2.5-32B

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: 32.5B
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
Hugging Face: Qwen

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: Qwen2.5: A Party of Foundation Models!
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run Qwen2.5 Instruct (32B)

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 Qwen2.5 Instruct (32B)

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

Hardware requirements in practice

Minimum card

RTX A4500

Memory needed

16.5 GB

Fastest

104 tok/s

With 32.5B parameters, Qwen2.5 Instruct (32B) lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

The entry point is the RTX A4500: 20 GB of memory, Q3_K_M compression, roughly 22.5 tokens per second.

A B200 is the fastest we calculate for it: about 104 tokens per second, from 8,000 GB/s of memory bandwidth.

What this model is

Qwen2.5 Instruct (32B) was published by Alibaba, in China, in September 2024. industry is the category the publisher falls under.

It works in Language, and is recorded as doing code generation, Code autocompletion, Quantitative reasoning, Question answering, Language modeling/generation.

It is derived from Qwen2.5-32B rather than trained from scratch, which is the usual way a specialised model is produced.

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

What decides the speed

Across every card that can run it, the middle of the range is about 20.3 tokens per second, and 102 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for Qwen2.5 Instruct (32B)

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 Qwen2.5 Instruct (32B) actually needs — around 16.5 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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: at long context Qwen2.5 Instruct (32B) can slip off a card that handles short questions easily.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Qwen2.5 Instruct (32B) — 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

Sort by speed to see how cards rank for Qwen2.5 Instruct (32B). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 104 tok/s.
05

Read the fit column last

Tight means Qwen2.5 Instruct (32B) loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
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 Qwen2.5 Instruct (32B) is settled.

Answers

Qwen2.5 Instruct (32B) — common questions

What is Qwen2.5 Instruct (32B) used for?

Qwen2.5 Instruct (32B) works in Language, and is recorded as handling code generation, Code autocompletion, Quantitative reasoning, Question answering, Language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Qwen2.5 Instruct (32B)?

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

Can I run Qwen2.5 Instruct (32B) if it does not fit in my GPU?

It can be split between the card and system memory, but Qwen2.5 Instruct (32B) generates painfully slowly that way — the nearest miss we calculate is short by 5.9 GB. Nothing on this page assumes offloading.

Would two GPUs run Qwen2.5 Instruct (32B) faster?

Capacity adds across cards; throughput does not. Since 132 of the cards we track already hold Qwen2.5 Instruct (32B) on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Qwen2.5 Instruct (32B)?

Because capacity varies, so does how hard Qwen2.5 Instruct (32B) has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Qwen2.5 Instruct (32B) speed estimates?

These are estimates with real error bars. The fastest result here, 89–125 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Qwen2.5 Instruct (32B)?

The smallest card in our catalogue that holds Qwen2.5 Instruct (32B) is the RTX A4500, with 20 GB of memory. It runs the model at Q3_K_M using about 16.5 GB, and produces roughly 22.5 tokens per second. 132 cards in total can run it.

How fast is Qwen2.5 Instruct (32B) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 104 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 102 of the cards that can run Qwen2.5 Instruct (32B) clear that.

How much VRAM does Qwen2.5 Instruct (32B) need?

About 16.5 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.

Can I run Qwen2.5 Instruct (32B) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 20.3 GB and generating roughly 40.3 tokens per second — a tight fit.

Is Qwen2.5 Instruct (32B) open source?

Its weights are published, so Qwen2.5 Instruct (32B) 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 Qwen2.5 Instruct (32B) have?

Qwen2.5 Instruct (32B) has 32.5B parameters. 32.5B. 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 Qwen2.5 Instruct (32B)?

Qwen2.5 Instruct (32B) was published by Alibaba, based in China, categorised as industry.

When was Qwen2.5 Instruct (32B) released?

Qwen2.5 Instruct (32B) was published in September 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.

Source

Original publication

Record last updated 11 February 2026

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