CogView TPS calculator

Open weights Tsinghua University,Alibaba DAMO Academy 4B parameters May 2021

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 · Q5_K_M · 16.5 tok/s

Fastest card

B200

847 tok/s · 180 GB

Which GPUs can run CogView?

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
847 tok/s 508–1,355 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	5.0 GB	Q8_0	Comfortable
847 tok/s 508–1,355 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	5.0 GB	Q8_0	Comfortable
676 tok/s 406–1,082 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	5.0 GB	Q8_0	Comfortable
676 tok/s 406–1,082 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	5.0 GB	Q8_0	Comfortable
541 tok/s 325–866 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
518 tok/s 311–828 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.0 GB	Q8_0	Comfortable
518 tok/s 311–828 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.0 GB	Q8_0	Comfortable
496 tok/s 297–793 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
417 tok/s 250–667 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 213–569 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 213–569 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	5.0 GB	Q8_0	Comfortable
356 tok/s 213–569 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 213–569 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 213–569 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
271 tok/s 163–433 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	5.0 GB	Q8_0	Comfortable
271 tok/s 163–433 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	5.0 GB	Q8_0	Comfortable
226 tok/s 135–361 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
221 tok/s 133–353 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	5.0 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	5.0 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	5.0 GB	Q8_0	Comfortable
216 tok/s 130–346 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	5.0 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: Tsinghua University,Alibaba DAMO Academy
Organisation type: Academia,Industry
Country: China
Published: 26 May 2021
Authors: Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

What it does

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

Domain: Image generation
Task: Text-to-image, Image generation
Approach: Self-supervised learning
Numerical format: FP16

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: 4B
Training data: 964,800,000,000 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: 2.7 × 10²² FLOP
How it was established: Third-party estimation

The training run

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

Training hardware: NVIDIA Tesla V100 DGXS 16 GB
Chips used: 512
Power draw: 259.1 kW
Compute cost: $60,072

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: Open source

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Foundation model: Yes
Why it is tracked: SOTA improvement
Record confidence: Likely
Citations: 979

Sources

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

Reference: CogView: Mastering Text-to-Image Generation via Transformers
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run CogView

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 CogView

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

3.6 GB

Fastest

847 tok/s

CogView reaches a parameter count of 4B. 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 Q5_K_M and producing around 16.5 tokens per second.

The quickest result comes from B200, generating roughly 847 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

CogView was published by Tsinghua University,Alibaba DAMO Academy, in the country recorded as China, during May 2021. The publishing organisation is categorised as academia,Industry.

It works in the domain of Image generation, and is recorded as performing the task of text-to-image, Image generation.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.

How fast it runs, and why

The median result is around 28.4 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 776 of them.

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 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.

How it was trained

Producing it required arithmetic totalling around 2.7 × 10²² FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 16 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Training consumed a corpus of around 964,800,000,000 tokens of text.

The reason it appears in this catalogue at all: sOTA improvement.

Step by step

How to choose a GPU for CogView

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 able to hold CogView, needing around 3.6 GB at a compression of Q5_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

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

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of Q5_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

Sort by speed

Sort by speed to see how cards rank for CogView. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 847 tok/s.
05

Read the fit column last

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of CogView. 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

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 CogView.

Answers

CogView — common questions

CogView— when was it released?

It was published in May 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

CogView— what is it used for?

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

CogView— 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.

CogView— how much compute was used to train it?

Training consumed around 2.7 × 10²² FLOP, on hardware recorded as NVIDIA Tesla V100 DGXS 16 GB. 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.

CogView— 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.

CogView— 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.

CogView— 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: 3. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

CogView— 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: 508–1,355 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

CogView— 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 Q5_K_M using about 3.6 GB, and produces roughly 16.5 tokens per second. The number of cards able to run it in total: 818.

CogView— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 847 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: 776.

CogView— how much VRAM does it need?

It needs about 3.6 GB at a compression of Q5_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.

CogView— 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 5.0 GB and generating roughly 158 tokens per second. The fit is comfortable.

CogView— 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 5.0 GB and generating roughly 96.6 tokens per second. The fit is comfortable.

CogView— 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 5.0 GB and generating roughly 120 tokens per second. The fit is comfortable.

CogView— 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 5.0 GB and generating roughly 142 tokens per second. The fit is comfortable.

CogView— 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.

CogView— how many parameters does it have?

It has a parameter count of 4B. "We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem.". 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.

CogView— who created it?

It was published by Tsinghua University,Alibaba DAMO Academy, based in China, an organisation categorised as academia,Industry.

Source

Original publication

Record last updated 25 May 2026

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