Cosmos-Predict2-2B-Text2Image TPS calculator

Open weights NVIDIA 2B parameters June 2025

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 · Q8_0 · 18.4 tok/s

Fastest card

B200

1,694 tok/s · 180 GB

Which GPUs can run Cosmos-Predict2-2B-Text2Image?

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
1,694 tok/s 1,016–2,711 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.8 GB	Q8_0	Comfortable
1,694 tok/s 1,016–2,711 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.8 GB	Q8_0	Comfortable
1,353 tok/s 812–2,164 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.8 GB	Q8_0	Comfortable
1,353 tok/s 812–2,164 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.8 GB	Q8_0	Comfortable
1,082 tok/s 649–1,731 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
1,036 tok/s 621–1,657 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.8 GB	Q8_0	Comfortable
1,036 tok/s 621–1,657 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.8 GB	Q8_0	Comfortable
991 tok/s 595–1,586 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
880 tok/s 528–1,407 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.8 GB	Q8_0	Comfortable
834 tok/s 501–1,335 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
712 tok/s 427–1,138 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.8 GB	Q8_0	Comfortable
542 tok/s 325–867 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.8 GB	Q8_0	Comfortable
542 tok/s 325–867 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.8 GB	Q8_0	Comfortable
451 tok/s 271–722 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
442 tok/s 265–707 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.8 GB	Q8_0	Comfortable
432 tok/s 259–691 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.8 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: NVIDIA
Organisation type: Industry
Country: United States of America
Published: 11 June 2025

What it does

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

Domain: Image generation
Task: Image generation, Text-to-image

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: 2B
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: nvidia

How it is classified

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

Record confidence: Confident

Sources

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

Reference: Cosmos-Predict2: A Suite of Diffusion-based World Foundation Models Available in 2B, and 14B
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Cosmos-Predict2-2B-Text2Image

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 Cosmos-Predict2-2B-Text2Image

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

Tesla C1080

Memory needed

2.8 GB

Fastest

1,694 tok/s

Cosmos-Predict2-2B-Text2Image is small enough at 2B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 18.4 tokens per second.

The quickest result comes from a B200 at around 1,694 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Cosmos-Predict2-2B-Text2Image was published by NVIDIA, in United States of America, in June 2025. The organisation is categorised as industry.

It works in Image generation, and is recorded as doing image generation, Text-to-image.

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

How fast it runs, and why

Across every card that can run it, the middle of the range is about 47.6 tokens per second, and 789 of them clear the ten tokens per second that roughly matches reading speed.

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

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

Step by step

How to choose a GPU for Cosmos-Predict2-2B-Text2Image

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

The table lists every card that can hold Cosmos-Predict2-2B-Text2Image — around 2.8 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
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 Cosmos-Predict2-2B-Text2Image stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage Cosmos-Predict2-2B-Text2Image by squeezing it further than you would want.
04

Sort by speed

The speed ordering for Cosmos-Predict2-2B-Text2Image is effectively an ordering by memory bandwidth, which is why the B200 tops it at 1,694 tok/s.
05

Check the fit verdict before buying

Tight means Cosmos-Predict2-2B-Text2Image loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
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. Worth a look before buying for Cosmos-Predict2-2B-Text2Image alone — a card is usually bought for more than one model.

Answers

Cosmos-Predict2-2B-Text2Image — common questions

Would two GPUs run Cosmos-Predict2-2B-Text2Image faster?

Two cards buy memory rather than speed. That matters for Cosmos-Predict2-2B-Text2Image only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Cosmos-Predict2-2B-Text2Image?

Each card is shown running the least-compressed copy it can hold, and Cosmos-Predict2-2B-Text2Image appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Cosmos-Predict2-2B-Text2Image speed estimates?

These are estimates with real error bars. The fastest result here, 1,016–2,711 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Cosmos-Predict2-2B-Text2Image?

The smallest card in our catalogue that holds Cosmos-Predict2-2B-Text2Image is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.8 GB, and produces roughly 18.4 tokens per second. 818 cards in total can run it.

How fast is Cosmos-Predict2-2B-Text2Image on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,694 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 789 of the cards that can run Cosmos-Predict2-2B-Text2Image clear that.

How much VRAM does Cosmos-Predict2-2B-Text2Image need?

About 2.8 GB at Q8_0 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.

Can I run Cosmos-Predict2-2B-Text2Image on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.8 GB and generating roughly 316 tokens per second — a comfortable fit.

Can I run Cosmos-Predict2-2B-Text2Image on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.8 GB and generating roughly 193 tokens per second — a comfortable fit.

Can I run Cosmos-Predict2-2B-Text2Image on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.8 GB and generating roughly 239 tokens per second — a comfortable fit.

Can I run Cosmos-Predict2-2B-Text2Image on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.8 GB and generating roughly 284 tokens per second — a comfortable fit.

Is Cosmos-Predict2-2B-Text2Image open source?

Its weights are published, so Cosmos-Predict2-2B-Text2Image 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 Cosmos-Predict2-2B-Text2Image have?

Cosmos-Predict2-2B-Text2Image has 2B 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 Cosmos-Predict2-2B-Text2Image?

Cosmos-Predict2-2B-Text2Image was published by NVIDIA, based in United States of America, categorised as industry.

When was Cosmos-Predict2-2B-Text2Image released?

Cosmos-Predict2-2B-Text2Image was published in June 2025.

What is Cosmos-Predict2-2B-Text2Image used for?

Cosmos-Predict2-2B-Text2Image works in Image generation, and is recorded as handling image generation, Text-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Cosmos-Predict2-2B-Text2Image?

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

Can I run Cosmos-Predict2-2B-Text2Image 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 Cosmos-Predict2-2B-Text2Image is rarely worth using. Every figure here assumes the whole model is on the card.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

All AI models