CogVideoX TPS calculator

Open weights Z.ai (Zhipu AI),Tsinghua University 5B parameters October 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · IQ4_XS · 18.1 tok/s

Fastest card

B200

678 tok/s · 180 GB

Which GPUs can run CogVideoX?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
678 tok/s 407–1,084 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	6.1 GB	Q8_0	Comfortable
678 tok/s 407–1,084 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	6.1 GB	Q8_0	Comfortable
541 tok/s 325–866 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	6.1 GB	Q8_0	Comfortable
541 tok/s 325–866 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	6.1 GB	Q8_0	Comfortable
433 tok/s 260–692 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	6.1 GB	Q8_0	Comfortable
414 tok/s 249–663 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	6.1 GB	Q8_0	Comfortable
414 tok/s 249–663 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	6.1 GB	Q8_0	Comfortable
396 tok/s 238–634 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	6.1 GB	Q8_0	Comfortable
352 tok/s 211–563 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	6.1 GB	Q8_0	Comfortable
352 tok/s 211–563 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	6.1 GB	Q8_0	Comfortable
352 tok/s 211–563 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	6.1 GB	Q8_0	Comfortable
334 tok/s 200–534 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	6.1 GB	Q8_0	Comfortable
285 tok/s 171–455 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	6.1 GB	Q8_0	Comfortable
285 tok/s 171–455 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	6.1 GB	Q8_0	Comfortable
285 tok/s 171–455 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	6.1 GB	Q8_0	Comfortable
285 tok/s 171–455 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	6.1 GB	Q8_0	Comfortable
285 tok/s 171–455 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	6.1 GB	Q8_0	Comfortable
217 tok/s 130–347 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	6.1 GB	Q8_0	Comfortable
217 tok/s 130–347 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	6.1 GB	Q8_0	Comfortable
181 tok/s 108–289 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	6.1 GB	Q8_0	Comfortable
177 tok/s 106–283 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	6.1 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	6.1 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	6.1 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	6.1 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	6.1 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: Z.ai (Zhipu AI),Tsinghua University
Organisation type: Industry,Academia
Country: China
Published: 8 October 2024
Authors: Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang

What it does

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

Domain: Video
Task: Video generation, Text-to-video

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: 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 (restricted use)
Training code: Open source
Hugging Face: THUDM

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: CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run CogVideoX

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 CogVideoX

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

3.4 GB

Fastest

678 tok/s

CogVideoX reaches a parameter count of 5B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 818.

The smallest card that holds it is Tesla C1080, with a memory capacity of 4 GB, running it at a compression of IQ4_XS and producing around 18.1 tokens per second.

Top of the range is B200, generating roughly 678 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

What this model is

CogVideoX was published by Z.ai (Zhipu AI),Tsinghua University, in the country recorded as China, during October 2024. It comes out of an organisation categorised as industry,Academia.

It works in the domain of Video, and is recorded as performing the task of video generation, Text-to-video.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. On Hugging Face it is published under the organisation THUDM.

What decides the speed

Half the cards that hold it manage more than 27.1 tokens per second. Producing text faster than most people read it: 771 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.

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 CogVideoX

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

01

Read the memory figure first

Every card here has been checked against CogVideoX, needing around 3.4 GB at a compression of IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

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 CogVideoX.
03

Choose how far you will compress it

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

Rank by throughput rather than spec sheet

Ranking by tokens per second follows memory bandwidth rather than core counts, for CogVideoX. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 678 tok/s.
05

Check the fit verdict before buying

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

See what else that card runs

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

Answers

CogVideoX — common questions

CogVideoX— how much VRAM does it need?

It needs about 3.4 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.

CogVideoX— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of Q8_0, using about 6.1 GB and generating roughly 126 tokens per second. The fit is tight.

CogVideoX— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q8_0, using about 6.1 GB and generating roughly 77.3 tokens per second. The fit is comfortable.

CogVideoX— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 6.1 GB and generating roughly 95.7 tokens per second. The fit is comfortable.

CogVideoX— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 6.1 GB and generating roughly 114 tokens per second. The fit is comfortable.

CogVideoX— 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.

CogVideoX— how many parameters does it have?

It has a parameter count of 5B. 5 billion. 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.

CogVideoX— who created it?

It was published by Z.ai (Zhipu AI),Tsinghua University, based in China, an organisation categorised as industry,Academia.

CogVideoX— when was it released?

It was published in October 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.

CogVideoX— what is it used for?

It works in the domain of Video, and is recorded as handling the task of video generation, Text-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.

CogVideoX— where can I download it?

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

CogVideoX— 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. Every figure here assumes the whole model is resident on the card.

CogVideoX— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 818. So a second card is rarely the answer here.

CogVideoX— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

CogVideoX— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 407–1,084 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

CogVideoX— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Tesla C1080, with a memory capacity of 4 GB. It runs the model at a compression of IQ4_XS using about 3.4 GB, and produces roughly 18.1 tokens per second. The number of cards able to run it in total: 818.

CogVideoX— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 678 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: 771.

Source

Original publication

Record last updated 28 November 2025

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