What GPU do I need to run nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-FP8?
49.9B parameters, published in F8_E4M3. View on Hugging Face
Set up Llama-3_3-N…per-49B-v1_5-FP8Llama-3_3-Nemotron-Super-49B-v1_5-FP8 is published by nvidia on Hugging Face, with 253,435 downloads and 28 likes to date. It's a DeciLMForCausalLM 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 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-FP8 at its published (F8_E4M3) precision: 1× RTX PRO 6000 on vastai, at $1.23/hr per GPU ($1.23/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-FP8: common questions
Can Llama-3_3-Nemotron-Super-49B-v1_5-FP8 run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 55.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 96.0 GB RTX PRO 6000 on vastai at $1.23/hr.
Is Llama-3_3-Nemotron-Super-49B-v1_5-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 55.7 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 27.9 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 Llama-3_3-Nemotron-Super-49B-v1_5-FP8 can run in?
27.9 GB, at INT4 (quantized), which fits a 32 GB card, against 55.7 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 Llama-3_3-Nemotron-Super-49B-v1_5-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX PRO 6000 on vastai at $1.23/hr. At INT4 (quantized) it drops to one RTX 8000 on akash at $0.221/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
- 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)
