What GPU do I need to run nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16?
4.0B parameters, published in BF16. View on Hugging Face
NVIDIA-Nemotron-3-Nano-4B-BF16 is published by nvidia on Hugging Face, with 377,865 downloads and 116 likes to date. It's a NemotronHForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
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-3-Nano-4B-BF16 at its published (BF16) precision: 1× RTX 5060 Ti on simplepod, at $0.100/hr per GPU ($0.100/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-3-Nano-4B-BF16: common questions
Does NVIDIA-Nemotron-3-Nano-4B-BF16 fit on a 12 GB GPU?
Yes. At BF16 it needs 8.9 GB of VRAM, so a 12 GB card holds it with 3.1 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM NVIDIA-Nemotron-3-Nano-4B-BF16 can run in?
2.2 GB, at INT4 (quantized), which fits a 6 GB card, against 8.9 GB at BF16. 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-3-Nano-4B-BF16 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 5060 Ti on simplepod at $0.100/hr. At INT4 (quantized) it drops to one RTX 3070 on simplepod at $0.050/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More nvidia models
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