Qwen2-72B TPS calculator

Open weights Alibaba 72.7B parameters June 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

61 of 818 cards that can run it

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 24.5 tok/s

Fastest card

B200

46.6 tok/s · 180 GB

Which GPUs can run Qwen2-72B?

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

61 cards match

Calculating

					Needs	Quantisation	Fit
46.6 tok/s 40–56	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	76.5 GB	Q8_0	Comfortable
46.6 tok/s 40–56	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	76.5 GB	Q8_0	Comfortable
37.2 tok/s 22–60 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	76.5 GB	Q8_0	Comfortable
37.2 tok/s 22–60 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	76.5 GB	Q8_0	Comfortable
29.8 tok/s 18–48 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	76.5 GB	Q8_0	Comfortable
28.5 tok/s 24–34	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	76.5 GB	Q8_0	Comfortable
28.5 tok/s 24–34	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	76.5 GB	Q8_0	Comfortable
28.4 tok/s 24–34	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	59.6 GB	Q6_K	Comfortable
28.4 tok/s 24–34	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	59.6 GB	Q6_K	Comfortable
27.3 tok/s 16–44 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	76.5 GB	Q8_0	Comfortable
25.2 tok/s 21–30	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	42.6 GB	Q4_K_M	Tight
24.5 tok/s 21–29	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	34.2 GB	Q3_K_M	Tight
24.5 tok/s 21–29	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	34.2 GB	Q3_K_M	Tight
24.5 tok/s 21–29	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	34.2 GB	Q3_K_M	Tight
24.2 tok/s 15–39 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	76.5 GB	Q8_0	Comfortable
24.2 tok/s 15–39 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	76.5 GB	Q8_0	Comfortable
24.2 tok/s 15–39 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	76.5 GB	Q8_0	Comfortable
23.0 tok/s 20–28	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	76.5 GB	Q8_0	Tight
21.0 tok/s 18–25	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	51.1 GB	Q5_K_M	Tight
19.6 tok/s 17–23	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q8_0	Tight
19.6 tok/s 17–23	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	76.5 GB	Q8_0	Tight
19.6 tok/s 17–23	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q8_0	Tight
18.0 tok/s 15–22	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	42.6 GB	Q4_K_M	Tight
17.3 tok/s 15–21	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	59.6 GB	Q6_K	Comfortable
17.3 tok/s 15–21	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	59.6 GB	Q6_K	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: 7 June 2024
Authors: An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng W…

What it does

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

Domain: Language
Task: Chat, Language modeling/generation, Question answering
Approach: Self-supervised learning
Numerical format: BF16

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: 72.7B
Training data: 7,000,000,000,000 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: 3 × 10²⁴ FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Data centre: There is no paper to reference, no information about hardware used for training found in media.

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.

Likely above 10²³ FLOP: Yes
Why it is tracked: Training cost
Record confidence: Confident

Sources

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

Reference: Hello Qwen2
Last updated: 18 December 2025

The extremes

The ten fastest GPUs for Qwen2-72B

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 Qwen2-72B

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

A100 PCIe 40 GB

Memory needed

34.2 GB

Fastest

46.6 tok/s

Qwen2-72B sits at 72.7B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.

The least hardware that works is a A100 PCIe 40 GB. Its 40 GB is enough at Q3_K_M compression, giving roughly 24.5 tokens per second.

The quickest result comes from a B200 at around 46.6 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Qwen2-72B was published by Alibaba, in China, in June 2024. It comes out of industry.

It works in Language, and is recorded as doing chat, Language modeling/generation, Question answering.

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 Qwen organisation on Hugging Face.

How fast it runs, and why

Half the cards that hold it manage more than 16.4 tokens per second, and 50 exceed reading speed outright.

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.

Because the architecture is recorded, the memory column is derived rather than estimated.

Training and provenance

The training run consumed about 3 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 7,000,000,000,000 tokens of text.

Its inclusion criterion is training cost.

Step by step

How to choose a GPU for Qwen2-72B

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

01

Check what it needs before anything else

The table lists every card that can hold Qwen2-72B — around 34.2 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Qwen2-72B.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of Qwen2-72B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Ranking by tokens per second for Qwen2-72B follows memory bandwidth, not core counts, which is why the B200 tops it at 46.6 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Qwen2-72B from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Qwen2-72B.

Answers

Qwen2-72B — common questions

What GPU do I need to run Qwen2-72B?

The smallest card in our catalogue that holds Qwen2-72B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 34.2 GB, and produces roughly 24.5 tokens per second. 61 cards in total can run it.

How fast is Qwen2-72B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 46.6 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 50 of the cards that can run Qwen2-72B clear that.

How much VRAM does Qwen2-72B need?

About 34.2 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.

Is Qwen2-72B open source?

Its weights are published, so Qwen2-72B 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 Qwen2-72B have?

Qwen2-72B has 72.7B parameters. 72.71B parameters in total, of which 70.21B are non-embedding 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.

Who created Qwen2-72B?

Qwen2-72B was published by Alibaba, based in China, categorised as industry.

When was Qwen2-72B released?

Qwen2-72B was published in June 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.

What is Qwen2-72B used for?

Qwen2-72B works in Language, and is recorded as handling chat, Language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Qwen2-72B?

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.

How much compute was used to train Qwen2-72B?

Around 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.

Can I run Qwen2-72B if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded Qwen2-72B is rarely worth using — the nearest miss we calculate is short by 13.8 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Qwen2-72B faster?

Capacity adds across cards; throughput does not. Since 61 of the cards we track already hold Qwen2-72B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Qwen2-72B?

Because capacity varies, so does how hard Qwen2-72B has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Qwen2-72B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 40–56 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Source

Original publication

Record last updated 18 December 2025

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