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

Libraries

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF",
	filename="Nemotron-3-Super-120B-Q4_K_M.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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with llama.cpp:

Install from brew

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

Use Docker

docker model run hf.co/baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

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

Unsloth Studio

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open /spaces/unsloth/studio in your browser
# Search for baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF to start chatting

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf baa-ai/Nemotron-3-Super-120B-A12B-MINT-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": "baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-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 baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Nemotron-3-Super-120B-A12B-MINT-GGUF

GGUF quantized version of nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 optimised by baa.ai.

Hybrid Mamba-MoE-Attention architecture (512 experts, 22 active per token) — Q4_K_M quantized for llama.cpp.

Files

| File | Quant | Size |

|------|-------|------|

| Nemotron-3-Super-120B-Q4_K_M.gguf | Q4_K_M | 80 GB |

Metrics

| Metric | Value |

|--------|-------|

| Size | 80 GB |

| Quantization | Q4_K_M |

| Framework | llama.cpp (GGUF) |

| Architecture | Hybrid Mamba-2 + MoE + Attention |

| Parameters | 123.6B (12B active) |

Usage


llama-cli -m Nemotron-3-Super-120B-Q4_K_M.gguf \

    -p "Hello!" -n 256 --threads 8

Notes

Requires llama.cpp build ≥ 8500 for NemotronH MoE + latent projection support
Peak memory: ~85 GB
MTP (Multi-Token Prediction) layers are stripped during conversion

Quantized by baa.ai

Black Sheep AI Products

Shepherd — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.

Watchman — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.

Learn more at baa.ai — Sovereign AI.

Downloads last month: 50

GGUF

Model size

121B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF

Base model

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

Quantized

(43)

this model

Collection including baa-ai/Nemotron-3-Super-120B-A12B-MINT-GGUF

Nemotron 3 Super

Collection

MINT quantized Nemotron-3-Super-120B — hybrid Mamba-MoE-Attention (MLX & GGUF) • 2 items • Updated Mar 27