Qwen-72B TPS calculator

Open weights Alibaba 72B parameters November 2023

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

43 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI200

64 GB · IQ4_XS · 18.5 tok/s

Fastest card

B200

47.1 tok/s · 180 GB

Which GPUs can run Qwen-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

43 cards match

Calculating

					Needs	Quantisation	Fit
47.1 tok/s 40–56	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	93.3 GB	Q8_0	Comfortable
47.1 tok/s 40–56	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	93.3 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	93.3 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	93.3 GB	Q8_0	Comfortable
35.3 tok/s 30–42	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	68.1 GB	Q5_K_M	Tight
35.3 tok/s 30–42	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
33.7 tok/s 29–40	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
30.1 tok/s 18–48 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	93.3 GB	Q8_0	Comfortable
29.2 tok/s 25–35	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	55.6 GB	IQ4_XS	Tight
28.8 tok/s 24–35	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	93.3 GB	Q8_0	Comfortable
28.8 tok/s 24–35	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	93.3 GB	Q8_0	Comfortable
28.7 tok/s 24–34	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
28.7 tok/s 24–34	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
28.7 tok/s 24–34	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
27.5 tok/s 17–44 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
21.4 tok/s 18–26	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
20.4 tok/s 17–24	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	68.1 GB	Q5_K_M	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: Alibaba
Organisation type: Industry
Country: China
Published: 30 November 2023
Authors: Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Sh…

What it does

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

Domain: Language
Task: Chat, Code generation, Language modeling/generation, Question answering
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: 72B
Training data: 3,000,000,000,000 tokens
Batch size: 4,000,000

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: 1.3 × 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: The paper does not mention any hardware, GPUs or any information regarding the hardware used.

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

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: SOTA improvement
Record confidence: Confident

Sources

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

Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Qwen-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 Qwen-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

The hardware side

Minimum card

Radeon Instinct MI200

Memory needed

55.6 GB

Fastest

47.1 tok/s

Qwen-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: 43.

At the low end it is handled by Radeon Instinct MI200, with a memory capacity of 64 GB, running it at a compression of IQ4_XS and producing around 18.5 tokens per second.

At the other end sits B200, generating roughly 47.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

Qwen-72B was published by Alibaba, in the country recorded as China, during November 2023. The category the publisher falls under is industry.

It works in the domain of Language, and is recorded as performing the task of chat, Code generation, Language modeling/generation, Question answering.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Understanding the speeds

Half the cards that hold it manage more than 21.4 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 39 of them.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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

What went into building it

Producing it required arithmetic totalling around 1.3 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

It was trained on a corpus of about 3,000,000,000,000 tokens of text.

Its inclusion criterion: sOTA improvement.

Step by step

How to choose a GPU for Qwen-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

Start from what it actually needs, which is the requirement of Qwen-72B, needing around 55.6 GB at a compression of IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason a card that seemed fine stops fitting Qwen-72B.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold, reaching a compression of IQ4_XS 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

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Qwen-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

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of Qwen-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

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

Answers

Qwen-72B — common questions

Qwen-72B— who created it?

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

Qwen-72B— when was it released?

It was published in November 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

Qwen-72B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of chat, Code generation, Language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Qwen-72B— where can I download it?

The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Qwen-72B— how much compute was used to train it?

Training consumed around 1.3 × 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.

Qwen-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 16.6 GB. Every figure here assumes the whole model is resident on the card.

Qwen-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: 43. So a second card is rarely the answer here.

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

Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Qwen-72B— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 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.

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

The smallest card in our catalogue that holds it is Radeon Instinct MI200, with a memory capacity of 64 GB. It runs the model at a compression of IQ4_XS using about 55.6 GB, and produces roughly 18.5 tokens per second. The number of cards able to run it in total: 43.

Qwen-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: 39.

Qwen-72B— how much VRAM does it need?

It needs about 55.6 GB at a compression of IQ4_XS, 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.

Qwen-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.

Qwen-72B— how many parameters does it have?

It has a parameter count of 72B. 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.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

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