What GPU do I need to run nvidia/Llama-3_3-Nemotron-Super-49B-v1_5?
49.9B parameters, published in BF16. View on Hugging Face
Llama-3_3-Nemotron-Super-49B-v1_5 is published by nvidia on Hugging Face, with 13,875 downloads and 235 likes to date. It's a DeciLMForCausalLM 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 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Llama-3_3-Nemotron-Super-49B-v1_5 at its published (BF16) precision: 7× RTX 5060 Ti on simplepod, at $0.100/hr per GPU ($0.700/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)
- parakeet-ctc-1.1b (1.1B, F32)
- NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (123.6B, BF16)
- Cosmos-Reason2-2B (2.4B, BF16)
- nemotron-3.5-asr-streaming-0.6b (638M, F32)
- NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B, BF16)
