Cosmos-1.0- Diffusion-14B Video2World TPS calculator
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 we hold specifications for
Smallest card that fits
P102-101
10 GB · IQ4_XS · 20.2 tok/s
Fastest card
B200
242 tok/s · 180 GB
Which GPUs can run Cosmos-1.0- Diffusion-14B Video2World?
Set the inputs, read the answer
A longer conversation needs more memory, which can push this model off smaller cards.
Hides cards that would only fit the model by compressing it below this point.
306 cards match
Calculating| Needs | Quantisation | Fit | |||||
|---|---|---|---|---|---|---|---|
|
242
tok/s
145–387 · low confidence |
B200 NVIDIA | 180 GB | 8,000 GB/s | Jan 2024 | 15.7 GB | Q8_0 | Comfortable |
|
242
tok/s
145–387 · low confidence |
B300 NVIDIA | 288 GB | 8,000 GB/s | Sep 2025 | 15.7 GB | Q8_0 | Comfortable |
|
193
tok/s
116–309 · low confidence |
Radeon Instinct MI350X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 15.7 GB | Q8_0 | Comfortable |
|
193
tok/s
116–309 · low confidence |
Radeon Instinct MI355X AMD | 288 GB | 8,190 GB/s | Jan 2025 | 15.7 GB | Q8_0 | Comfortable |
|
155
tok/s
93–247 · low confidence |
Radeon Instinct MI300 AMD | 128 GB | 6,550 GB/s | Jan 2023 | 15.7 GB | Q8_0 | Comfortable |
|
148
tok/s
89–237 · low confidence |
H200 NVL NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 15.7 GB | Q8_0 | Comfortable |
|
148
tok/s
89–237 · low confidence |
H200 SXM 141 GB NVIDIA | 141 GB | 4,890 GB/s | Nov 2024 | 15.7 GB | Q8_0 | Comfortable |
|
142
tok/s
85–227 · low confidence |
Radeon Instinct MI325X AMD | 256 GB | 6,000 GB/s | Oct 2024 | 15.7 GB | Q8_0 | Comfortable |
|
126
tok/s
75–201 · low confidence |
Radeon Instinct MI300A AMD | 128 GB | 5,325 GB/s | Dec 2023 | 15.7 GB | Q8_0 | Comfortable |
|
126
tok/s
75–201 · low confidence |
Radeon Instinct MI300X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 15.7 GB | Q8_0 | Comfortable |
|
126
tok/s
75–201 · low confidence |
Radeon Instinct MI308X AMD | 192 GB | 5,325 GB/s | Dec 2023 | 15.7 GB | Q8_0 | Comfortable |
|
119
tok/s
72–191 · low confidence |
H100 NVL 94 GB NVIDIA | 94 GB | 3,940 GB/s | Mar 2023 | 15.7 GB | Q8_0 | Comfortable |
|
116
tok/s
70–185 · low confidence |
CMP 170HX 10 GB NVIDIA | 10 GB | 1,560 GB/s | Sep 2021 | 8.4 GB | IQ4_XS | Tight |
|
102
tok/s
61–163 · low confidence |
H100 PCIe 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 15.7 GB | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
H100 SXM5 80 GB NVIDIA | 80 GB | 3,360 GB/s | Oct 2022 | 15.7 GB | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
H100 SXM5 94 GB NVIDIA | 94 GB | 3,360 GB/s | Mar 2023 | 15.7 GB | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
H100 SXM5 96 GB NVIDIA | 96 GB | 3,360 GB/s | Mar 2023 | 15.7 GB | Q8_0 | Comfortable |
|
102
tok/s
61–163 · low confidence |
H800 SXM5 NVIDIA | 80 GB | 3,360 GB/s | Mar 2023 | 15.7 GB | Q8_0 | Comfortable |
|
77.4
tok/s
46–124 · low confidence |
Radeon Instinct MI250 AMD | 128 GB | 3,280 GB/s | Nov 2021 | 15.7 GB | Q8_0 | Comfortable |
|
77.4
tok/s
46–124 · low confidence |
Radeon Instinct MI250X AMD | 128 GB | 3,280 GB/s | Nov 2021 | 15.7 GB | Q8_0 | Comfortable |
|
64.5
tok/s
39–103 · low confidence |
Data Center GPU Max 1550 Intel | 128 GB | 3,280 GB/s | Jan 2023 | 15.7 GB | Q8_0 | Comfortable |
|
63.1
tok/s
38–101 · low confidence |
Data Center GPU Max Subsystem Intel | 128 GB | 3,210 GB/s | Jan 2023 | 15.7 GB | Q8_0 | Comfortable |
|
61.7
tok/s
37–99 · low confidence |
A100 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Nov 2020 | 15.7 GB | Q8_0 | Comfortable |
|
61.7
tok/s
37–99 · low confidence |
A100X NVIDIA | 80 GB | 2,040 GB/s | Jun 2021 | 15.7 GB | Q8_0 | Comfortable |
|
61.7
tok/s
37–99 · low confidence |
A800 SXM4 80 GB NVIDIA | 80 GB | 2,040 GB/s | Aug 2022 | 15.7 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
- 7 January 2025
- Authors
- NVIDIA: Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, Daniel Dworakowski, Jiaojiao Fan, Michele Fenzi, Francesco Ferroni, Sanja Fidler, Dieter Fox, Songwei Ge, Yunhao Ge, Jinwei Gu, Siddharth Gururani, Ethan He, Jiahui Huang, Jacob Huffman, Pooya Jannaty, Jingyi Jin, Seung Wook Kim, Gergely Klár, Grace …
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics, Vision, Video
- Task
- Robotic manipulation, Self-driving car, 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
- 14B
- Training data
- 9,000,000,000,000,000 tokens
14B
"Suite of first-generation video models trained on 9,000 trillion tokens, including 20 million hours of robotics and driving data - generating high-quality videos from multimodal inputs like images, text, or video." - https://www.nvidia.com/en-us/ai/cosmos/
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
- 2.8 × 10²⁴ FLOP
- How it was established
- Hardware
"We train all of the WFM models reported in the paper using a cluster of 10,000 NVIDIA H100 GPUs in a time span of three months." I assign the FLOPs from this cluster proportional to the parameter size of the model trained. There are a total of 76B parameters between the 8 models. Therefore, assuming 20% utilization (starting with 33% but then accounting for time between experiments), we get (10k H100s)*(90 days)*(24*60*60)*(979e12)*(0.2 utilization)*(14/76) = 2.8e24 FLOPs
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 H100 SXM5 80GB
- Chips used
- 10,000
- Power draw
- 13.8 MW
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
- Hugging Face
- nvidia
NVIDIA Open Model License Agreement Under the NVIDIA Open Model License, NVIDIA confirms: Models are commercially usable. You are free to create and distribute Derivative Models. NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models. Important Note: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or aut…
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
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Cosmos World Foundation Model Platform for Physical AI
- Last updated
- 28 November 2025
The extremes
The ten fastest GPUs that run Cosmos-1.0- Diffusion-14B 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.
- 01 B300 288 GB · 8,000 GB/s · Q8_0 242 tok/s
- 02 B200 180 GB · 8,000 GB/s · Q8_0 242 tok/s
- 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 193 tok/s
- 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 193 tok/s
- 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 155 tok/s
- 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 148 tok/s
- 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 148 tok/s
- 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 142 tok/s
- 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 126 tok/s
- 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 126 tok/s
The smallest GPUs that still run Cosmos-1.0- Diffusion-14B Video2World
The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.
- 01 Arc B570 10 GB · needs 8.4 GB · IQ4_XS · tight 18.4 tok/s
- 02 Xbox Series X 6nm GPU 10 GB · needs 8.4 GB · IQ4_XS · tight 32.5 tok/s
- 03 Radeon RX 6750 GRE 10 GB 10 GB · needs 8.4 GB · IQ4_XS · tight 18.5 tok/s
- 04 CMP 170HX 10 GB 10 GB · needs 8.4 GB · IQ4_XS · tight 116 tok/s
- 05 CMP 90HX 10 GB · needs 8.4 GB · IQ4_XS · tight 56.5 tok/s
- 06 CMP 50HX 10 GB · needs 8.4 GB · IQ4_XS · tight 41.6 tok/s
- 07 Radeon RX 6700 10 GB · needs 8.4 GB · IQ4_XS · tight 18.5 tok/s
- 08 Radeon RX 6700M 10 GB · needs 8.4 GB · IQ4_XS · tight 18.5 tok/s
- 09 Xbox Series X GPU 10 GB · needs 8.4 GB · IQ4_XS · tight 32.5 tok/s
- 10 GeForce RTX 3080 10 GB · needs 8.4 GB · IQ4_XS · tight 56.5 tok/s
What the numbers mean
What it takes to run this model
Minimum card
P102-101
Memory needed
8.4 GB
Fastest
242 tok/s
Cosmos-1.0- Diffusion-14B Video2World is small enough at 14B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.
The smallest card that holds it is the P102-101 with 10 GB, running it at IQ4_XS and producing around 20.2 tokens per second.
At the other end, a B200 generates roughly 242 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.
Background
Cosmos-1.0- Diffusion-14B Video2World was published by NVIDIA, in United States of America, in January 2025. The organisation is categorised as industry.
It works in Robotics, Vision, Video, and is recorded as doing robotic manipulation, Self-driving car, Video generation.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the nvidia organisation on Hugging Face.
Reading the throughput figures
The median result is around 20.4 tokens per second; 268 cards produce text faster than most people read it.
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.
What went into building it
Producing it required around 2.8 × 10²⁴ FLOP of arithmetic, on NVIDIA H100 SXM5 80GB, which is a statement about the training budget rather than about inference.
Around 9,000,000,000,000,000 tokens went into training it.
Step by step
How to choose a GPU for Cosmos-1.0- Diffusion-14B Video2World
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
Every card here has been checked against Cosmos-1.0- Diffusion-14B Video2World — around 8.4 GB at IQ4_XS. Capacity is the gate — a card either holds it or it does not.
-
02
Decide how long your conversations run
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-1.0- Diffusion-14B Video2World.
-
03
Decide how much compression you will accept
The quantisation column varies by card, because a bigger card holds a more accurate copy of Cosmos-1.0- Diffusion-14B Video2World — IQ4_XS on the smallest card that fits. Set a floor to hold the comparison at one level.
-
04
Sort by speed
Sort by speed to see how cards rank for Cosmos-1.0- Diffusion-14B Video2World. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 242 tok/s.
-
05
Check the fit verdict before buying
Tight means Cosmos-1.0- Diffusion-14B Video2World 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
Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Cosmos-1.0- Diffusion-14B Video2World.
Answers
Cosmos-1.0- Diffusion-14B Video2World — common questions
Who created Cosmos-1.0- Diffusion-14B Video2World?
Cosmos-1.0- Diffusion-14B Video2World was published by NVIDIA, based in United States of America, categorised as industry.
When was Cosmos-1.0- Diffusion-14B Video2World released?
Cosmos-1.0- Diffusion-14B Video2World was published in January 2025.
What is Cosmos-1.0- Diffusion-14B Video2World used for?
Cosmos-1.0- Diffusion-14B Video2World works in Robotics, Vision, Video, and is recorded as handling robotic manipulation, Self-driving car, Video generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Cosmos-1.0- Diffusion-14B 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.
How much compute was used to train Cosmos-1.0- Diffusion-14B Video2World?
Around 2.8 × 10²⁴ FLOP, on NVIDIA H100 SXM5 80GB. 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.
Can I run Cosmos-1.0- Diffusion-14B 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-1.0- Diffusion-14B Video2World is rarely worth using — the nearest miss we calculate is short by 2.0 GB. Every figure here assumes the whole model is on the card.
Would two GPUs run Cosmos-1.0- Diffusion-14B Video2World faster?
Capacity adds across cards; throughput does not. Since 306 of the cards we track already hold Cosmos-1.0- Diffusion-14B Video2World on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for Cosmos-1.0- Diffusion-14B Video2World?
Because capacity varies, so does how hard Cosmos-1.0- Diffusion-14B Video2World has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.
How accurate are these Cosmos-1.0- Diffusion-14B Video2World speed estimates?
They are calculated from specifications rather than measured, and each carries a range — 145–387 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.
What GPU do I need to run Cosmos-1.0- Diffusion-14B Video2World?
The smallest card in our catalogue that holds Cosmos-1.0- Diffusion-14B Video2World is the P102-101, with 10 GB of memory. It runs the model at IQ4_XS using about 8.4 GB, and produces roughly 20.2 tokens per second. 306 cards in total can run it.
How fast is Cosmos-1.0- Diffusion-14B Video2World on a GPU?
It depends on the card. The quickest we calculate is a B200 at about 242 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 268 of the cards that can run Cosmos-1.0- Diffusion-14B Video2World clear that.
How much VRAM does Cosmos-1.0- Diffusion-14B Video2World need?
About 8.4 GB at IQ4_XS 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-1.0- Diffusion-14B Video2World on a 12 GB GPU?
Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q5_K_M, using about 10.8 GB and generating roughly 49.3 tokens per second — a tight fit.
Can I run Cosmos-1.0- Diffusion-14B Video2World on a 16 GB GPU?
Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 12.4 GB and generating roughly 49.7 tokens per second — a tight fit.
Can I run Cosmos-1.0- Diffusion-14B Video2World on a 24 GB GPU?
Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 15.7 GB and generating roughly 40.5 tokens per second — a comfortable fit.
Is Cosmos-1.0- Diffusion-14B Video2World open source?
Its weights are published, so Cosmos-1.0- Diffusion-14B 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-1.0- Diffusion-14B Video2World have?
Cosmos-1.0- Diffusion-14B Video2World has 14B parameters. 14B. 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.
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.