NVIDIA·Nemotron·NemotronHForCausalLM

NVIDIA Nemotron 3 Ultra 550B A55B GenRM — Hardware Requirements & GPU Compatibility

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NVIDIA Nemotron 3 Ultra 550B A55B GenRM is a 560.5B-parameter open language model from NVIDIA in the Nemotron family. 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.

164 downloads 9 likes262K context

Specifications

Publisher
NVIDIA
Family
Nemotron
Parameters
560.5B
Architecture
NemotronHForCausalLM
Context Length
262,144 tokens
Vocabulary Size
131,072
Release Date
2026-05-26
License
Other

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How Much VRAM Does NVIDIA Nemotron 3 Ultra 550B A55B GenRM Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.40262.1 GB
Q3_K_Mest.3.90300.6 GB
Q4_K_Mest.4.80369.9 GB
Q5_K_Mest.5.70439.3 GB
Q6_Kest.6.60508.7 GB
Q8_0est.8.00616.6 GB
BF16est.16.001233.2 GB

est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.

Which GPUs Can Run NVIDIA Nemotron 3 Ultra 550B A55B GenRM?

Q4_K_M · 369.9 GB

NVIDIA Nemotron 3 Ultra 550B A55B GenRM (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 GenRM?

Q4_K_M · 369.9 GB

3 devices with unified memory can run NVIDIA Nemotron 3 Ultra 550B A55B GenRM, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 512GB).

Decent

Enough memory, may be tight

Related Models

Frequently Asked Questions

How much VRAM does NVIDIA Nemotron 3 Ultra 550B A55B GenRM need?

NVIDIA Nemotron 3 Ultra 550B A55B GenRM requires 369.9 GB of VRAM at Q4_K_M, or 1233.2 GB at BF16.

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

369.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron 3 Ultra 550B A55B GenRM?

No — NVIDIA Nemotron 3 Ultra 550B A55B GenRM requires at least 262.1 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for NVIDIA Nemotron 3 Ultra 550B A55B GenRM?

For NVIDIA Nemotron 3 Ultra 550B A55B GenRM, Q4_K_M (369.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (439.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 262.1 GB.

VRAM requirement by quantization

Q2_K
262.1 GB
Q4_K_M
369.9 GB
Q5_K_M
439.3 GB
Q6_K
508.7 GB
Q8_0
616.6 GB
BF16
1233.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run NVIDIA Nemotron 3 Ultra 550B A55B GenRM on a Mac?

NVIDIA Nemotron 3 Ultra 550B A55B GenRM requires at least 262.1 GB at Q2_K, 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 GenRM locally?

Yes — NVIDIA Nemotron 3 Ultra 550B A55B GenRM 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 GenRM?

At Q4_K_M, the download is about 336.31 GB. The full-precision BF16 version is 1121.05 GB. The smallest option (Q2_K) is 238.22 GB.

Which GPUs can run NVIDIA Nemotron 3 Ultra 550B A55B GenRM?

No single consumer GPU has enough VRAM to run NVIDIA Nemotron 3 Ultra 550B A55B GenRM at Q4_K_M (369.9 GB). Multi-GPU or professional hardware is required.

Which devices can run NVIDIA Nemotron 3 Ultra 550B A55B GenRM?

3 devices with unified memory can run NVIDIA Nemotron 3 Ultra 550B A55B GenRM at Q4_K_M (369.9 GB), including Mac Studio (M3 Ultra, 512GB), 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.