What GPU do I need to run RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block?
560.5B parameters, published in F8_E4M3. View on Hugging Face
Set up NVIDIA-Nemo…B-A55B-FP8-blockNVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block is published by RedHatAI on Hugging Face, with 18,169 downloads and 0 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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 522.0 GB | 626.4 GB | AMD MI300X | 4 | $10.52/hr |
| INT4 (quantized) | 261.0 GB | 313.2 GB | RTX A6000 | 7 | $2.54/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 NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block at its published (F8_E4M3) precision: 4× AMD MI300X, at $2.63/hr per GPU ($10.52/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-Ultra-550B-A55B-FP8-block: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block is not published for this architecture, so no figure is shown.
NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block: common questions
Can NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block run on a single GPU?
No. At FP8 (native) it needs 626.4 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 192.0 GB AMD MI300X, and it takes 4 of them.
Is NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block already quantized?
Yes. It is published in FP8, one byte per parameter, so the 626.4 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 313.2 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.
How many GPUs do I need to run NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block?
4 at FP8 (native). It needs 626.4 GB of VRAM and the cheapest capable live offer is a 192.0 GB AMD MI300X, so 4 of them come to $10.52/hr in total.
Does quantizing NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is 4 AMD MI300X cards at $10.52/hr. At INT4 (quantized) it drops to 7 RTX A6000 cards at $2.54/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Nemotron 3 models
- NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 (560.5B, BF16)
- NVIDIA-Nemotron-3-Super-120B-A12B-BF16 (123.6B, BF16)
- NVIDIA-Nemotron-3-Super-120B-A12B-FP8 (123.6B, F8_E4M3)
- NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16 (123.6B, BF16)
- NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 (31.6B, F8_E4M3)
Lineage
Based on NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.
Alternatives at this size
Other models for text-generation within about a third of NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block's 560.5B parameters, from other model lines.
- DeepSeek-R1 (684.5B, F8_E4M3)
- GLM-5.2 (753.3B, BF16)
- Ornith-1.5-397B (403.4B, BF16)
- Llama-3.1-405B (405.9B, BF16)
- A.X-K2 (691.7B, F8_E4M3)
More on NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
