Instructions to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF",
	filename="IQ3_S/NVIDIA-Nemotron-3-Super-120B-A12B-BF16-IQ3_S-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Ollama:
```
ollama run hf.co/AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
```

Unsloth Studio

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF to start chatting

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Docker Model Runner:
```
docker model run hf.co/AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M
```

Lemonade

How to use AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AesSedai/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NVIDIA-Nemotron-3-Super-120B-A12B-GGUF-Q4_K_M

List all available models

lemonade list

IQ2XXS for us on the edge?

by RyanoSaurus-Wrex - opened Mar 12

Discussion

RyanoSaurus-Wrex

Mar 12

Hey thanks for your work, I have your Qwen 3.5 122B@iQ2XXS quant and seems solid (granted haven't gone too deep with him) but saw you never updated it and wonder why? Could you maybe do one for this new Nemotron? For the 32R/16VR crowd trying to hold on? Also did you find something wrong with your other iQ2XXS and is that why you didn't update the quant like all the others?

AesSedai

Owner Mar 12

Hi @rkh661, honestly I wasn't really aware people were using the IQ2_XXS so when I redid them I just did my usual line-up. I don't think that 32+16 is really viable for this model since the ffn_down_exps can't be quanted below Q4_0. Even unsloth's smallest quants are 52.7GB so you just aren't going to get lower than that really.

I'll redo the IQ2_XXS with the fused expert for the Qwen3.5-122B and upload it though.

RyanoSaurus-Wrex

Mar 13

Ok @AesSedai , I gotcha. with some of these newer architecture's they don't play nice going down to the redline. But thanks anyways and appreciate you uploading the updated iQ2XXS for Qwen 3.5 122B! He's been sharp and some of these big guys can go low, if you throw every trick we have at em with hybrid quants, and imatrix, and all the rest, so thank you sir! 🫡

AesSedai

Owner Mar 13

@rkh661 The fused Qwen3.5-122B-A10B IQ2_XSS has been uploaded.

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