Quick answer: no, and the gap is not close. NVIDIA’s published support matrix for Cosmos 3 asks for at least 79 GiB of VRAM per device even for the small 8B “nano” variant, on a GPU with Hopper architecture or newer. The largest consumer card sold today is the 32GB RTX 5090. This is not a quantisation problem you can optimise around — it is a two-and-a-half-times gap on a card that also fails the architecture requirement.
If you have read that Cosmos 3 Nano runs on an RTX 4090 at INT4, that claim does not survive contact with NVIDIA’s documentation. The section below shows exactly where the two accounts diverge.
What NVIDIA actually requires
From the NVIDIA NIM for Cosmos support matrix, version 3.0.0:
| Variant | Precision | Per-device VRAM |
|---|---|---|
| 8B (nano) | any supported precision | ≥ 79 GiB |
| 32B (super) | fp8 | ≥ 121 GiB single, ≥ 79 GiB across 2 GPUs |
| 32B (super) | bf16 | ≥ 150 GiB single, ≥ 92 GiB across 2, ≥ 65 GiB across 4 |
| 32B (super) | nvfp4 | ≥ 131 GiB single |
Two constraints matter as much as the numbers. The model runs on Hopper or later (compute capability ≥ 9.0), which rules out every GeForce card before Blackwell regardless of memory. And nvfp4 additionally requires Blackwell (≥ 10.0), so the most memory-efficient precision is also the most hardware-restricted.
Note the shape of the nano row: NVIDIA gives the same floor for every supported precision. That is worth pausing on, because it is precisely the assumption the optimistic guides break.
Why “8B should fit in 8GB” is wrong here
The arithmetic that works for a language model does not transfer. An 8B model at 4-bit is roughly 4-5GB of weights, which is why a 4090 runs 8B language models comfortably — and it is almost certainly where the “Cosmos 3 Nano fits at INT4” figure comes from.
Cosmos 3 is not a chat model. It is an omnimodal world model built for physical AI, and the weights are the small part of its footprint. Generation carries activation memory and intermediate state that scale with resolution and frame count, not with parameter count. NVIDIA’s own figure for 720p video generation on the super variant peaks around 83 GB, falling to roughly 55 GB with fp8 across two GPUs — numbers that have no relationship to the size of a weights file.
This is the general trap. Reading a parameter count and reaching for the usual “params × bytes” rule produces a number that is correct for the weights and irrelevant for the workload. Wherever a model generates video or long image sequences, ask what the documentation says about peak memory rather than computing it from the parameter count.
Who this is for
This guide is for anyone who saw Cosmos 3 top an open-weights leaderboard and wondered whether their desktop can run it. The short answer is above. If you are choosing a GPU for image or video generation that actually runs locally, best GPU for AI video covers the models that do, and best GPU for Wan 2.2 is the closest local equivalent in spirit.
Which GPU should you buy?
None of them, for this model. That is the honest answer, and it is more useful than a stretch recommendation. What to do instead:
- You want Cosmos 3 specifically: rent. An H100 or H200 meets both the memory floor and the architecture requirement, and for occasional generation the economics are not close — even sustained daily use would take years to justify buying hardware in this class, assuming you could.
- You want local video generation: buy for a model that fits. Wan 2.2 and LTX-Video run on consumer cards, and a 24GB RTX 4090 at ~$2,200 covers both comfortably.
- You want local video on a budget: the RTX 4070 Ti Super at ~$800 is the entry point our Wan 2.2 guide settles on — 16GB handles 14B int8 at 720p, with longer render times than a 4090.
- You want the best local image quality: that is a different question with a much better answer — see best GPU for Flux.
- You are building for physical AI research: you are in datacenter territory by NVIDIA’s own spec, and the purchase decision belongs with whoever funds the cluster.
Common mistakes
- Trusting a parameter count over the vendor’s support matrix. 8B sounds small. NVIDIA asks for 79 GiB anyway, and the vendor’s own requirements beat any figure derived from arithmetic.
- Assuming quantisation always rescues you. NVIDIA lists the same per-device floor for every supported precision on the nano variant. Precision choices here change compatibility more than they change the memory requirement.
- Ignoring the architecture gate. Compute capability ≥ 9.0 excludes Ada and everything before it on memory-independent grounds. A hypothetical 80GB GeForce card still would not qualify.
- Buying a 5090 because it is the biggest consumer card. 32GB is an excellent amount of VRAM for local LLM and image work. It is not within reach of this model, and buying it for Cosmos 3 specifically would be a wasted $4,900.
Final verdict
Cosmos 3 is a genuinely impressive open-weights release and it is not for your desktop. NVIDIA’s support matrix asks for 79 GiB per device on the smallest variant and Hopper-generation silicon or newer, which puts every consumer GPU out of reach twice over.
The useful takeaway is not about this model. It is that a parameter count is not a memory requirement for anything that generates video, and when a vendor publishes a support matrix, that document beats any figure a guide computed for you — including ours.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB at ~$2,200 handles Wan 2.2 and LTX-Video comfortably, which is where local video generation actually happens.
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Frequently asked questions
Can an RTX 4090 run NVIDIA Cosmos 3?
No. NVIDIA’s support matrix asks for at least 79 GiB of VRAM per device even for the 8B nano variant, against the 4090’s 24GB, and separately requires Hopper architecture or newer, which the Ada-generation 4090 does not meet.
Why do some guides say Cosmos 3 Nano fits in 8GB?
Because they estimated from the parameter count rather than reading the requirements. An 8B model at 4-bit is roughly 4-5GB of weights, but Cosmos 3 generates video, and activation memory during generation dominates the weights entirely.
What can I run locally instead?
Wan 2.2 and LTX-Video both run on consumer cards, and a 24GB RTX 4090 handles either comfortably. If you specifically need Cosmos 3, renting an H100 or H200 by the hour is the practical route.