🧩NVIDIA Nemotron 3 Ultra - How To Run Locally

Run Nemotron-3-Ultra-550B-A55B locally on your device!

NVIDIA Nemotron 3 Ultra is an open 550B parameter, 55B active frontier-reasoning model and is NVIDIA's largest model released so far. Nemotron-3-Ultra-550B-A55B is built for long-running autonomous agents and reasoning across coding, deep research workflows. It is the strongest Western open model, and adopts the new Open Model, Weights & Data License.

With up to 1M context, Nemotron 3 Ultra uses a Hybrid Transformer-Mamba MoE architecture and can preserve long agent state, logs, and plans across sustained sessions. GGUFs are at Nemotron-3-Ultra-550B-A55B with dynamic 1bit taking 189GB of disk space. It's also pretrained using NVFP4. We als did GGUF KLD Benchmarks.

⚙️ Usage Guide

NVIDIA recommends these settings for inference:

• temperature = 1.0

• top_p = 0.95

Detail	Nemotron 3 Ultra
Model size	550B total parameters / 55B active parameters
Context length	Up to 1M tokens
Architecture	Hybrid Transformer-Mamba MoE with Latent MoE, Multi-Token Prediction (MTP currently not supported for GGUFs)
Model I/O	Text input, text output

The chat template is like below:

<|im_start|>system\n<|im_end|>\n<|im_start|>user\nWhat is 1+1?<|im_end|>\n<|im_start|>assistant\n<think></think>2<|im_end|>\n<|im_start|>assistant\n<think>\n

Run Nemotron-3-Ultra

The 3-bit versions of the model requires ~256GB RAM, 4-bit needs ~300GB and 8-bit requires 600GB. For these guides, we will be using 3-bit UD-IQ3_XXS which fits on a 256GB device and is a good balance between size and accuracy. Depending on your use-case you will need to use different settings. GGUF: Nemotron-3-Ultra-550B-A55B

Run in Unsloth StudioRun in llama.cpp

🦥 Unsloth Studio Guide

For this tutorial, we will be using Unsloth Studio, which is our UI for running and training LLMs. With Unsloth Studio, you can run models and input image and text locally on Mac, Windows, and Linux and:

• Search, download, run GGUFs and safetensor models

• Compare models side-by-side

• Self-healing tool calling + web search

• Code execution (Python, Bash)

• Automatic inference parameter tuning (temp, top-p, etc.)

• Train LLMs 2x faster with 70% less VRAM

1

Install Unsloth

MacOS, Linux, WSL:

curl -fsSL https://unsloth.ai/install.sh | sh

Windows PowerShell:

irm https://unsloth.ai/install.ps1 | iex

2

Setup Unsloth Studio (one time)

Setup automatically installs Node.js (via nvm), builds the frontend, installs all Python dependencies, and builds llama.cpp with CUDA support.

WSL users: you will be prompted for your sudo password to install build dependencies (cmake, git, libcurl4-openssl-dev).

3

Launch Unsloth

MacOS, Linux, WSL:

source unsloth_studio/bin/activate
unsloth studio -H 0.0.0.0 -p 8888

Windows Powershell:

unsloth studio

Then open http://127.0.0.1:8888 in your browser.

4

Search and download Nemotron-3-Ultra

On first launch you will need to create a password to secure your account and sign in again later. Then go to the Studio Chat tab and search for Nemotron-3-Ultra in the search bar and download your desired model and quant.

5

Run Nemotron-3-Ultra

Inference parameters should be auto-set when using Unsloth Studio, however you can still change it manually. You can also edit the context length, chat template and other settings.

For more information, you can view our Unsloth Studio inference guide.

6

Serving Nemotron-3-Ultra

You can also use unsloth studio run to serve the model via llama-server like so:

unsloth studio run --model unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF:UD-Q4_K_XL

🦙 Llama.cpp Tutorial:

Instructions to run in llama.cpp (note we will be using 4-bit to fit most devices):

1

Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. For Apple Mac / Metal devices, set -DGGML_CUDA=OFF then continue as usual - Metal support is on by default.

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

2

Download the model via the code below (after installing pip install huggingface_hub). You can choose Q4_K_M or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging

pip install huggingface_hub
hf download unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF \
    --local-dir unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF \
    --include "*UD-IQ3_XXS*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit

3

Then run the model in conversation mode:

./llama.cpp/llama-cli \
    --model unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF/UD-IQ3_XXS/NVIDIA-Nemotron-3-Ultra-550B-A55B-UD-IQ3_XXS-00001-of-00006.gguf \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01

Llama-server serving & deployment

To deploy Nemotron-3-Ultra locally, use llama-server. In a new terminal, for example via tmux, deploy the model:

./llama.cpp/llama-server \
    -hf unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF:UD-IQ3_XXS \
    --alias "unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B" \
    --temp 1.0 \
    --top-p 0.95 \
    --port 8001

If you downloaded the model manually, use:

./llama.cpp/llama-server \
    --model unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B-GGUF/UD-IQ3_XXS/NVIDIA-Nemotron-3-Ultra-550B-A55B-UD-IQ3_XXS-00001-of-00006.gguf \
    --alias "unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B" \
    --temp 1.0 \
    --top-p 0.95 \
    --port 8001

Then in a new terminal, after installing the OpenAI client with pip install openai:

from openai import OpenAI
openai_client = OpenAI(
    base_url = "http://127.0.0.1:8001/v1",
    api_key = "sk-no-key-required",
)
completion = openai_client.chat.completions.create(
    model = "unsloth/NVIDIA-Nemotron-3-Ultra-550B-A55B",
    messages = [
        {"role": "user", "content": "What is 2+2?"},
    ],
)
print(completion.choices[0].message.reasoning_content)
print(completion.choices[0].message.content)

And on 4 B200s, around 40 tokens / s is seen for generation!

Unsloth GGUF Benchmarks

We also did KLD analysis for our GGUF quants - on a log mean KLD scale, the model loses very little accuracy when quantized down to even 1bit due to our dynamic methodology where more important layers are left in higher precision and the rest in lower bits.

For a linear scale:

Official Benchmarks

Nemotron 3 Ultra is NVIDIA's largest Nemotron 3 reasoning model and is positioned for leading accuracy on frontier reasoning, coding and agentic tasks while optimizing time to task completion through high throughput.

Ultra is especially suited for workloads where task success depends on sustained reasoning rather than short single-turn responses:

• Autonomous coding sessions across large repositories

• Deep research across many sources with conflicting evidence

• Enterprise workflows with persistent tool-using loops

• EDA / chip design verification and failure analysis

As shown in Figure 1 and Figure 2 Nemotron 3 Ultra leads on accuracy on agent productivity, instruction following, and long context tasks and provides leading throughout, saving 30% on costs compared to other leading open models.

Figure 1: Nemotron 3 Ultra leads among open models on agentic benchmarks for agent productivity, coding, and instruction following.

Figure 2: Nemotron 3 Ultra saves up to 30% in costs and leads on the cost efficiency frontier

Image showing Nemotron 3 Ultra saves up to 30% in costs and leads on the cost efficiency frontier

More benchmarks from NVIDIA:

Benchmark	N-3-Ultra 550B-A55B	MiniMax-2.7 230B-A10B	GLM-5.1 744B-A40B	Kimi-K2.6 1T-A32B
Agentic
Terminal Bench 2.1	56.4	55.5	59.3	67.2	49.9	49.2	54.2
GDPVal	46.7	47.6	54.7	50.4	34.6	54.6	50.2
SWE-Bench Verified	71.9	72.2	73.8	69.5	69.9	74.0	72.4
SWE-Bench Multilingual	67.7	69.2	73.8	65.9	67.7	71.9	72.1
ProfBench (Search)	56.0	52.0	46.0	56.0	53.0	59.9	57.0
PinchBench	90.0	77.6	81.2	90.2	86.6	88.6	91.3
TauBench V3
Airline	81.5	75.3	85.0	85.8	76.5	80.8	80.8
Retail	86.4	84.9	84.1	82.9	88.5	88.9	89.1
Telecom	92.9	89.6	96.9	97.8	98.0	96.3	98.3
Banking	22.6	14.6	12.8	23.1	20.9	25.9	26.7
Average	70.9	66.1	69.7	72.4	71.0	73.2	73.7
BrowseComp	44.4	54.1	59.4	61.3	40.5	59.4	46.9
Vals.ai Financial Agent 1.1
without web search	60.1	51.3	60.2	54.0	61.3	58.9	58.4
with web search	53.7	50.5	60.7	58.8	59.0	62.3	60.1
Reasoning and Knowledge
IOI 2025	570.0	--	456.5	585.0	441.3	580.1	--
LiveCodeBench (v6)	89.0	77.2	85.7	90.2	79.3	92.5	90.9
IMOAnswerBench (no tools)	88.6	68.3	86.8	91.1	83.1	93.0	91.1
IMOAnswerBench (with tools)	92.3	75.1	91.1	93.71	84.51	85.4	89.6
Apex-Shortlist (no tools)	74.9	28.9	71.1	77.4	61.4	85.8	82.4
Apex-Shortlist (with tools)	84.8	51.9	79.0	73.2	60.4	86.5	82.0
GPQA (no tools)	87.0	86.6	86.1	91.0	87.1	87.8	88.5
SciCode (subtask)	44.6	38.3	47.7	52.0	48.0	50.5	48.2
HLE (no tools)	26.7	23.1	27.2	34.8	28.5	37.7	32.2
HLE (with tools)	37.4	--	50.4	54.0	48.3	48.2	45.1
CritPt (no tools)	3.1	0.6	3.7	9.1	2.4	14.0	10.6
MMLU-Pro	86.8	81.9	85.9	88.1	88.3	87.5	86.4
OmniScience Accuracy	24.1	20.5	31.3	35.5	35.9	46.8	39.9
OmniScience Non-Hallucination	78.7	74.4	66.8	67.1	7.4	5.7	2.8
Chat & Instruction Following
IFBench (prompt loose)	81.7	74.6	76.6	73.7	78.2	79.1	82.0
Multi-Challenge	63.8	42.5	63.0	63.1	63.9	64.1	63.5
Long Context
AA-LCR	65.4	69.8	66.9	70.2	68.3	67.3	62.7
RULER (1M)	94.7	--	--	--	90.1	94.2	87.7
Longbench v2 (≤ 1M)	61.9	--	--	--	68.9	62.1	57.0
Multilingual
MMLU-ProX (avg en/de/fr/es/it/ja/zh/hi/pt/ko)	83.0	78.4	85.8	85.0	86.4	85.6	84.3
WMT24++ (en→xx)	83.7	82.8	84.4	84.5	86.8	85.9	85.9