NVIDIA Nemotron 3 Ultra 550B A55B BF16 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron 3 Ultra 550B A55B BF16 is a 560.5B-parameter open language model from NVIDIA. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 369.95 GB of VRAM — see which GPUs and Macs can run it below.
47.3K downloads
146 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 560.5B
- Architecture
- NemotronHForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-06-06
- License
- Other
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How Much VRAM Does NVIDIA Nemotron 3 Ultra 550B A55B BF16 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 262.1 GB | — | 238.22 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 300.6 GB | — | 273.26 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 369.9 GB | — | 336.31 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 439.3 GB | — | 399.37 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 508.7 GB | — | 462.43 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 616.6 GB | — | 560.52 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run NVIDIA Nemotron 3 Ultra 550B A55B BF16?
Q4_K_M · 369.9 GBNVIDIA Nemotron 3 Ultra 550B A55B BF16 (Q4_K_M) requires 369.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 481+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run NVIDIA Nemotron 3 Ultra 550B A55B BF16?
Q4_K_M · 369.9 GB2 devices with unified memory can run NVIDIA Nemotron 3 Ultra 550B A55B BF16, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron 3 Ultra 550B A55B BF16 need?
NVIDIA Nemotron 3 Ultra 550B A55B BF16 requires 369.9 GB of VRAM at Q4_K_M, or 616.6 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 560.5B × 4.8 bits ÷ 8 = 336.3 GB
KV Cache + Overhead ≈ 33.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M369.9 GB- Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron 3 Ultra 550B A55B BF16?
No — NVIDIA Nemotron 3 Ultra 550B A55B BF16 requires at least 169.6 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for NVIDIA Nemotron 3 Ultra 550B A55B BF16?
For NVIDIA Nemotron 3 Ultra 550B A55B BF16, Q4_K_M (369.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (423.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 169.6 GB.
VRAM requirement by quantization
IQ2_XXS169.6 GBQ2_K262.1 GBIQ4_NL346.8 GBQ4_K_M ★369.9 GBQ5_K_S423.9 GBQ8_0616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run NVIDIA Nemotron 3 Ultra 550B A55B BF16 on a Mac?
NVIDIA Nemotron 3 Ultra 550B A55B BF16 requires at least 169.6 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run NVIDIA Nemotron 3 Ultra 550B A55B BF16 locally?
Yes — NVIDIA Nemotron 3 Ultra 550B A55B BF16 can run locally on consumer hardware. At Q4_K_M quantization it needs 369.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of NVIDIA Nemotron 3 Ultra 550B A55B BF16?
At Q4_K_M, the download is about 336.31 GB. The full-precision Q8_0 version is 560.52 GB. The smallest option (IQ2_XXS) is 154.14 GB.
- Which GPUs can run NVIDIA Nemotron 3 Ultra 550B A55B BF16?
No single consumer GPU has enough VRAM to run NVIDIA Nemotron 3 Ultra 550B A55B BF16 at Q4_K_M (369.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run NVIDIA Nemotron 3 Ultra 550B A55B BF16?
2 devices with unified memory can run NVIDIA Nemotron 3 Ultra 550B A55B BF16 at Q4_K_M (369.9 GB), including NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.