Cosmos-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image?
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
- 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
- Text-to-image, Image 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
- 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
NVIDIA license (termination clause + attribution requirements) https://huggingface.co/nvidia/Cosmos-Predict2-14B-Text2Image Apache 2.0 for code https://github.com/nvidia-cosmos/cosmos-predict2/tree/main
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-Predict2-14B-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.
- 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-Predict2-14B-Text2Image
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-Predict2-14B-Text2Image 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.
Top of the range is the B200, at roughly 242 tokens per second thanks to 8,000 GB/s of bandwidth.
What this model is
Cosmos-Predict2-14B-Text2Image was published by NVIDIA, in United States of America, in June 2025. industry is the category the publisher falls under.
It works in Image generation, and is recorded as doing text-to-image, Image 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.
What decides the speed
Across every card that can run it, the middle of the range is about 20.4 tokens per second, and 268 of them clear the ten tokens per second that roughly matches reading speed.
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.
Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.
Step by step
How to choose a GPU for Cosmos-Predict2-14B-Text2Image
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 Cosmos-Predict2-14B-Text2Image — around 8.4 GB at IQ4_XS. 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-Predict2-14B-Text2Image.
-
03
Choose how far you will compress it
Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage Cosmos-Predict2-14B-Text2Image by squeezing it further than you would want.
-
04
Rank by throughput rather than spec sheet
The speed ordering for Cosmos-Predict2-14B-Text2Image is effectively an ordering by memory bandwidth, which is why the B200 tops it at 242 tok/s.
-
05
Look at the headroom, not just the fit
Tight means Cosmos-Predict2-14B-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
See what else that card runs
Following a card through to its own page shows every other model it can hold, which is the question that follows once Cosmos-Predict2-14B-Text2Image is settled.
Answers
Cosmos-Predict2-14B-Text2Image — common questions
Can I run Cosmos-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image open source?
Its weights are published, so Cosmos-Predict2-14B-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-Predict2-14B-Text2Image have?
Cosmos-Predict2-14B-Text2Image has 14B 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-Predict2-14B-Text2Image?
Cosmos-Predict2-14B-Text2Image was published by NVIDIA, based in United States of America, categorised as industry.
When was Cosmos-Predict2-14B-Text2Image released?
Cosmos-Predict2-14B-Text2Image was published in June 2025.
What is Cosmos-Predict2-14B-Text2Image used for?
Cosmos-Predict2-14B-Text2Image works in Image generation, and is recorded as handling text-to-image, Image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Cosmos-Predict2-14B-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-Predict2-14B-Text2Image if it does not fit in my GPU?
It can be split between the card and system memory, but Cosmos-Predict2-14B-Text2Image generates painfully slowly that way — the nearest miss we calculate is short by 2.0 GB. Nothing on this page assumes offloading.
Would two GPUs run Cosmos-Predict2-14B-Text2Image faster?
Capacity adds across cards; throughput does not. Since 306 of the cards we track already hold Cosmos-Predict2-14B-Text2Image on their own, a second card is rarely the answer here.
Why does the quantisation differ between cards for Cosmos-Predict2-14B-Text2Image?
Each card is shown running the least-compressed copy it can hold, and Cosmos-Predict2-14B-Text2Image appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.
How accurate are these Cosmos-Predict2-14B-Text2Image speed estimates?
These are estimates with real error bars. The fastest result here, 145–387 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-Predict2-14B-Text2Image?
The smallest card in our catalogue that holds Cosmos-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image clear that.
How much VRAM does Cosmos-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image 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-Predict2-14B-Text2Image 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.
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.