Cosmos-Predict2-2B-Video2World 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-Video2World?

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: Video, Vision, Robotics
Task: Robotic manipulation, System control, Video generation

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.

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: NVIDIA Releases New AI Models and Developer Tools to Advance Autonomous Vehicle Ecosystem
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Cosmos-Predict2-2B-Video2World

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-Video2World

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-Video2World 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.

At the other end, a B200 generates roughly 1,694 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

About this model

Cosmos-Predict2-2B-Video2World was published by NVIDIA, in United States of America, in June 2025. It comes out of industry.

It works in Video, Vision, Robotics, and is recorded as doing robotic manipulation, System control, Video generation.

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

The median result is around 47.6 tokens per second; 789 cards produce text faster than most people read it.

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.

Step by step

How to choose a GPU for Cosmos-Predict2-2B-Video2World

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

Every card here has been checked against Cosmos-Predict2-2B-Video2World — around 2.8 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Cosmos-Predict2-2B-Video2World.
03

Set a quality floor

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-Video2World by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Cosmos-Predict2-2B-Video2World. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,694 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Cosmos-Predict2-2B-Video2World from those with room to spare. Buy for the second if the context might grow.
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-Video2World alone — a card is usually bought for more than one model.

Answers

Cosmos-Predict2-2B-Video2World — common questions

Would two GPUs run Cosmos-Predict2-2B-Video2World faster?

Two cards buy memory rather than speed. That matters for Cosmos-Predict2-2B-Video2World 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-Video2World?

Because capacity varies, so does how hard Cosmos-Predict2-2B-Video2World has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Cosmos-Predict2-2B-Video2World 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-Video2World?

The smallest card in our catalogue that holds Cosmos-Predict2-2B-Video2World 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-Video2World 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-Video2World clear that.

How much VRAM does Cosmos-Predict2-2B-Video2World 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-Video2World 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-Video2World 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-Video2World 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-Video2World 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-Video2World open source?

Its weights are published, so Cosmos-Predict2-2B-Video2World 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-Video2World have?

Cosmos-Predict2-2B-Video2World 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-Video2World?

Cosmos-Predict2-2B-Video2World was published by NVIDIA, based in United States of America, categorised as industry.

When was Cosmos-Predict2-2B-Video2World released?

Cosmos-Predict2-2B-Video2World was published in June 2025.

What is Cosmos-Predict2-2B-Video2World used for?

Cosmos-Predict2-2B-Video2World works in Video, Vision, Robotics, and is recorded as handling robotic manipulation, System control, Video generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Cosmos-Predict2-2B-Video2World?

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-Video2World 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-Video2World 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