Instructions to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"

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 mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Nemotron-3-Super-120B-A12B — MLX 6-bit

MLX quantization of nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 for Apple Silicon.

Key Specs

Detail	Value
Architecture	Hybrid Mamba-2 + Transformer Attention + Latent MoE
Total Parameters	120B
Active Parameters	12B per token
Context Length	1M tokens (262,144 default)
Experts	512 routed, 22 active per token, 1 shared
Quantization	6-bit affine (6.507 BPW), group size 64
Disk Size	~92 GB
Peak Memory	~98.4 GB

Requirements

Apple Silicon Mac with 128GB+ unified memory
mlx-lm >= 0.31.2 (install from git main for Latent MoE support)

pip install git+https://github.com/ml-explore/mlx-lm.git

Usage

CLI

mlx_lm.generate \
  --model FF-01/Nemotron-3-Super-120B-A12B-MLX-6bit \
  --prompt "Hello!" \
  --max-tokens 256

Python

from mlx_lm import load, generate

model, tokenizer = load("FF-01/Nemotron-3-Super-120B-A12B-MLX-6bit")
response = generate(model, tokenizer, prompt="Hello!", max_tokens=256)
print(response)

LM Studio

This model is compatible with LM Studio on Apple Silicon. Search for FF-01/Nemotron-3-Super-120B-A12B-MLX-6bit in the model browser and download directly.

Performance

Tested on M5 Pro Max (128GB):

Metric	Value
Generation Speed	~43.6 tok/s
Peak Memory	98.4 GB

About the Architecture

Nemotron-H is a hybrid architecture combining three components:

Mamba-2 layers — efficient state-space model for long-context processing
Transformer attention layers — standard multi-head attention (GQA, 32 heads, 2 KV heads)
Latent MoE — 512 experts with latent routing, 22 active per token, plus 1 shared expert

The layer pattern alternates between Mamba (M) and attention with MoE (E) blocks across 88 layers. This hybrid design achieves strong performance with only 12B active parameters per token despite having 120B total.

Reasoning Model

This is a reasoning model that outputs chain-of-thought before the final answer. The model uses <think> and </think> tags to delineate reasoning.

License

NVIDIA Open Model License

Credits

Base model by NVIDIA
MLX quantization by FF-01

Downloads last month: 732

Safetensors

Model size

121B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

6-bit

Model tree for mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit

Base model

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

Quantized

(51)

this model