Qwen2-Math-72B TPS calculator

Open weights Alibaba 72B parameters August 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 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 24.8 tok/s

Fastest card

B200

47.1 tok/s · 180 GB

Which GPUs can run Qwen2-Math-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.

Search cards

Card memory

Also show cards that cannot run it

61 cards match

Calculating

					Needs	Quantisation	Fit
47.1 tok/s 40–56	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	74.5 GB	Q8_0	Comfortable
47.1 tok/s 40–56	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	74.5 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	74.5 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	74.5 GB	Q8_0	Comfortable
30.1 tok/s 18–48 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	74.5 GB	Q8_0	Comfortable
28.8 tok/s 24–35	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	74.5 GB	Q8_0	Comfortable
28.8 tok/s 24–35	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	74.5 GB	Q8_0	Comfortable
28.7 tok/s 24–34	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	57.8 GB	Q6_K	Comfortable
28.7 tok/s 24–34	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	57.8 GB	Q6_K	Comfortable
27.5 tok/s 17–44 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	74.5 GB	Q8_0	Comfortable
25.4 tok/s 22–30	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	41.0 GB	Q4_K_M	Tight
24.8 tok/s 21–30	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	32.6 GB	Q3_K_M	Tight
24.8 tok/s 21–30	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	32.6 GB	Q3_K_M	Tight
24.8 tok/s 21–30	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	32.6 GB	Q3_K_M	Tight
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	74.5 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	74.5 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	74.5 GB	Q8_0	Comfortable
23.2 tok/s 20–28	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	74.5 GB	Q8_0	Tight
21.2 tok/s 18–25	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	49.4 GB	Q5_K_M	Tight
19.8 tok/s 17–24	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	74.5 GB	Q8_0	Tight
19.8 tok/s 17–24	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	74.5 GB	Q8_0	Tight
19.8 tok/s 17–24	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	74.5 GB	Q8_0	Tight
18.2 tok/s 15–22	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	41.0 GB	Q4_K_M	Tight
17.4 tok/s 15–21	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	57.8 GB	Q6_K	Comfortable
17.4 tok/s 15–21	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	57.8 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: 9 August 2024

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, Mathematical reasoning
Base model: Qwen2-72B

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: 72B
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 (restricted use)
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
Record confidence: Confident

Sources

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

Reference: Introducing Qwen2-Math
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Qwen2-Math-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-Math-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

32.6 GB

Fastest

47.1 tok/s

Qwen2-Math-72B reaches a parameter count of 72B. 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: 61.

The least hardware that works is A100 PCIe 40 GB, with a memory capacity of 40 GB, running it at a compression of Q3_K_M and producing around 24.8 tokens per second.

The fastest we calculate for it is B200, generating roughly 47.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

Qwen2-Math-72B was published by Alibaba, in the country recorded as China, during August 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, Question answering, Mathematical reasoning.

Its starting point was an existing base model, Qwen2-72B. That is the usual way a specialised model is produced.

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. On Hugging Face it is published under the organisation Qwen.

What decides the speed

Half the cards that hold it manage more than 16.6 tokens per second. Exceeding reading speed outright: 49 of them.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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

Step by step

How to choose a GPU for Qwen2-Math-72B

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

Start from what it actually needs, which is the requirement of Qwen2-Math-72B, needing around 32.6 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

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 Qwen2-Math-72B.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy, 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

Rank by throughput rather than spec sheet

The speed ordering is effectively an ordering by memory bandwidth, for Qwen2-Math-72B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 47.1 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage it from those with room to spare, in the case of Qwen2-Math-72B. 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

Check the card from the other side

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 Qwen2-Math-72B.

Answers

Qwen2-Math-72B — common questions

Qwen2-Math-72B— can I run it 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 model is rarely worth using. The nearest miss we calculate falls short by 12.2 GB. Every figure here assumes the whole model is resident on the card.

Qwen2-Math-72B— 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: 61. So a second card is rarely the answer here.

Qwen2-Math-72B— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Qwen2-Math-72B— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 40–56 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Qwen2-Math-72B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is A100 PCIe 40 GB, with a memory capacity of 40 GB. It runs the model at a compression of Q3_K_M using about 32.6 GB, and produces roughly 24.8 tokens per second. The number of cards able to run it in total: 61.

Qwen2-Math-72B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 47.1 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: 49.

Qwen2-Math-72B— how much VRAM does it need?

It needs about 32.6 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.

Qwen2-Math-72B— 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.

Qwen2-Math-72B— how many parameters does it have?

It has a parameter count of 72B. 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.

Qwen2-Math-72B— who created it?

It was published by Alibaba, based in China, an organisation categorised as industry.

Qwen2-Math-72B— when was it released?

It was published in August 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.

Qwen2-Math-72B— 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, Mathematical reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.

Qwen2-Math-72B— where can I download it?

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

Source

Original publication

Record last updated 28 November 2025

The other direction

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