Nemotron 3 Ultra 550B A55B: VRAM Requirements & Local Setup Guide 2026
NVIDIA's 550B MoE model with 55B active parameters per token. Enterprise-grade open model requiring significant hardware.
Quick Facts
- Provider
- NVIDIA
- Architecture
- Mixture-of-Experts (MoE)
- Total Parameters
- 550B
- Active Params/Token
- 55B
- Context Window
- 262K tokens
- License
- open
- Modalities
- text
- HuggingFace
- nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
VRAM Requirements by Quantization & Context Length
All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.
| Quantization | 4K ctx | 8K ctx | 32K ctx | 128K ctx |
|---|---|---|---|---|
| Q4_K_M | 314.0 GB | 314.1 GB | 314.3 GB | 315.5 GB |
| Q5_K_M | 366.5 GB | 366.6 GB | 366.8 GB | 368.0 GB |
| Q6_K | 422.2 GB | 422.3 GB | 422.5 GB | 423.7 GB |
| Q8_0 | 545.8 GB | 545.8 GB | 546.1 GB | 547.2 GB |
| FP16 | 1026.0 GB | 1026.0 GB | 1026.3 GB | 1027.5 GB |
Weights = 550B params × GGUF bits per weight (Q4_K_M ≈ 4.9) — all experts are loaded; plus runtime overhead; plus fp16 KV cache from config.json: 2 KV heads × 128 dims (96 of 108 layers without a KV cache) — ~12 KB per token at short context. Same engine as the Can I Run LLM calculator.
Best GPU for Nemotron 3 Ultra 550B A55B by Budget
Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.
- Under $500 (Entry GPU) Not feasible at this budget
- $500–$1,000 (Mid-Range GPU) Not feasible at this budget
- $1,000–$3,000 (High-End GPU) Not feasible at this budget
- $3,000+ (Workstation / Cloud) Multi-GPU H100 cluster
Benchmark Scores
Scores reported by NVIDIA or verified third-party evaluations. Higher is better for all benchmarks except where noted.
| Benchmark | Score |
|---|---|
| SWE-bench Verified | 70.7 |
| LiveCodeBench | 89 |
| MMLU-Pro | 86.8 |
How to Run Nemotron 3 Ultra 550B A55B Locally
Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.
HuggingFace Hub
Nemotron 3 Ultra 550B A55B is available on HuggingFace. Download with:
pip install huggingface_hub huggingface-cli download nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
Strengths and Use Cases
Largest open model from NVIDIA. 55B active parameters places it in the top tier for reasoning tasks. Requires multi-GPU enterprise setup.
When choosing a local LLM, Nemotron 3 Ultra 550B A55B is worth considering if your workload aligns with its design goals. As a Mixture-of-Experts (MoE) architecture from NVIDIA, it offers a distinct trade-off between compute efficiency and capability. With MoE architecture, only a fraction of parameters are active at inference time, dramatically reducing VRAM requirements compared to a dense model of equivalent total size. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.
For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.
Ready to Check Your Hardware?
Use our interactive calculator to see exactly whether your GPU can run Nemotron 3 Ultra 550B A55B — and at what quantization level.
Related Tools