What GPU do I need to run nvidia/NVIDIA-Nemotron-Nano-9B-v2-FP8?
8.9B parameters, published in F8_E4M3. View on Hugging Face
NVIDIA-Nemotron-Nano-9B-v2-FP8 is published by nvidia on Hugging Face, with 191,959 downloads and 9 likes to date. It's a NemotronHForCausalLM model built for text-generation, published natively in F8_E4M3.
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 caveat: 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-9B-v2-FP8 at its published (F8_E4M3) precision: 1× RTX 4070 Super on simplepod, at $0.090/hr per GPU ($0.090/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More nvidia models
- Gemma-4-31B-IT-NVFP4 (20.9B, BF16)
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- nemotron-3.5-asr-streaming-0.6b (638M, F32)
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B, BF16)
- NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (123.6B, BF16)
- parakeet-tdt-0.6b-v3 (627M, F32)
