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
Set up Llama-3_3-N…n-Super-49B-v1_5Llama-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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 92.9 GB | 111.5 GB | RTX 5060 Ti | 7 | $0.770/hr |
| FP8 (quantized) | 46.4 GB | 55.7 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr | ||
| INT4 (quantized) | 23.2 GB | 27.9 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/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 Llama-3_3-Nemotron-Super-49B-v1_5 at its published (BF16) precision: 7× RTX 5060 Ti, at $0.110/hr per GPU ($0.770/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3_3-Nemotron-Super-49B-v1_5: common questions
Can Llama-3_3-Nemotron-Super-49B-v1_5 run on a single GPU?
No. At BF16 it needs 111.5 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 16.0 GB RTX 5060 Ti, and it takes 7 of them.
How many GPUs do I need to run Llama-3_3-Nemotron-Super-49B-v1_5?
7 at BF16. It needs 111.5 GB of VRAM and the cheapest capable live offer is a 16.0 GB RTX 5060 Ti, so 7 of them come to $0.770/hr in total.
What is the least VRAM Llama-3_3-Nemotron-Super-49B-v1_5 can run in?
27.9 GB, at INT4 (quantized), which fits a 32 GB card, against 111.5 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 Llama-3_3-Nemotron-Super-49B-v1_5 lower the GPU bill?
Yes. At BF16 the cheapest live fit is 7 RTX 5060 Ti cards at $0.770/hr. At INT4 (quantized) it drops to one RTX 4080 Super at $0.338/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.3 models
- Llama-3_3-Nemotron-Super-49B-v1_5-FP8 (49.9B, F8_E4M3)
- Llama-3_3-Nemotron-Super-49B-v1 (49.9B, BF16)
- Llama-3.3-70B-Instruct (70.6B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
