What GPU do I need to run nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8?
13.2B parameters, published in F8_E4M3. View on Hugging Face
Set up NVIDIA-Nemo…no-12B-v2-VL-FP8NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 is published by nvidia on Hugging Face, with 125,728 downloads and 51 likes to date. It's a unlisted-architecture model built for image-text-to-text, published natively in F8_E4M3.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 12.3 GB | 14.7 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 6.1 GB | 7.4 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 at its published (F8_E4M3) precision: 1× RTX 4000 SFF Ada, at $0.198/hr per GPU ($0.198/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
NVIDIA-Nemotron-Nano-12B-v2-VL-FP8: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 is not published for this architecture, so no figure is shown.
NVIDIA-Nemotron-Nano-12B-v2-VL-FP8: common questions
Does NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 fit on a 16 GB GPU?
Yes. At FP8 (native) it needs 14.7 GB of VRAM, so a 16 GB card holds it with 1.3 GB to spare. A 12 GB card is not enough for it at FP8 (native).
Is NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 14.7 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 7.4 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
What is the least VRAM NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 can run in?
7.4 GB, at INT4 (quantized), which fits an 8 GB card, against 14.7 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX 4000 SFF Ada at $0.198/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Nemotron Nano models
- NVIDIA-Nemotron-Nano-12B-v2 (12.3B, BF16)
- NVIDIA-Nemotron-Nano-9B-v2-FP8 (8.9B, F8_E4M3)
- NVIDIA-Nemotron-Nano-9B-v2 (8.9B, BF16)
Alternatives at this size
Other models for image-text-to-text within about a third of NVIDIA-Nemotron-Nano-12B-v2-VL-FP8's 13.2B parameters, from other model lines.
- Qwen3.5-9B (9.7B, BF16)
- gemma-3-12b-it (12.2B, BF16)
- Ovis2.5-9B (9.2B, BF16)
- Kimi-VL-A3B-Instruct (16.4B, BF16)
- GLM-4.1V-9B-Thinking (10.3B, BF16)
More on NVIDIA-Nemotron-Nano-12B-v2-VL-FP8
Fits on a 16 GB GPU at FP8: every model that fits in 16 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Related reading: H100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
