Instructions to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

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 mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

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 mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

LM Studio
Jan
Ollama
How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
```
Unsloth Desktop

How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16
```

Lemonade

How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

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 mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16

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 "mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

⚡ Each donation = another big MoE quantized

I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.

🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors

💚 Big thanks to Hugging Face for generously donating additional storage, much appreciated.

Nemotron-3-Nano-Omni-30B-A3B-Reasoning — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16.

Brought to you by the LocalAI team | APEX Project | Technical Report

Available Files

File	Profile	Size	Best For
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Balanced.gguf	I-Balanced	26 GB	Best overall quality/size ratio
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Balanced.gguf	Balanced	26 GB	General purpose
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Quality.gguf	I-Quality	22 GB	Highest quality with imatrix
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Quality.gguf	Quality	22 GB	Highest quality standard
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Compact.gguf	I-Compact	19 GB	Consumer GPUs, best quality/size
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-Compact.gguf	Compact	19 GB	Consumer GPUs
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Mini.gguf	I-Mini	18 GB	Smallest "safe" tier
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Nano.gguf	I-Nano	17 GB	Experimental — IQ2_XXS mid-layer experts
Nemotron-3-Nano-Omni-30B-A3B-Reasoning-F16.gguf	F16 reference	59 GB	Full-precision reference (text-only)
mmproj.gguf	Vision+audio projector	~1.6 GB	Required for image and audio understanding

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only 6/128 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.

See the APEX project for full details, technical report, and scripts.

Nano (experimental tier)

The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2_S, edges to Q3_K, with shared experts kept at Q5_K. About 5% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.

Benchmarks pending. Feedback welcome.

Multimodal Support

This is the Omni variant — supports text + vision + audio inputs. The included mmproj.gguf (sourced from unsloth) provides:

Vision: RADIO ViT encoder (1280-dim)
Audio: Parakeet encoder (1024-dim, 24 layers)

Pass --mmproj mmproj.gguf to llama.cpp / LocalAI to enable multimodal inference. Note: llama.cpp's audio output is not yet supported in mtmd — audio input only.

Architecture

Outer model: NemotronH_Nano_Omni_Reasoning_V3 (multimodal wrapper)
Inner LLM: NemotronH (NemotronHForCausalLM) — same as Nemotron-3-Nano-30B-A3B
Layers: 52 (23 Mamba-2, 23 MoE, 6 attention) per pattern MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME
Experts: 128 routed + 1 shared (6 active per token)
Total Parameters: 30B (LLM only) + RADIO + Parakeet
Active Parameters: ~3.5B per token
Hidden size: 2688
Context: 262,144 tokens
APEX Config: 5+5 symmetric edge gradient across 52 layers
Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF@Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-I-Balanced.gguf

Credits

Base model: NVIDIA Nemotron team
Vision+audio mmproj: unsloth
APEX quantization: LocalAI team
Built on llama.cpp (with PR #22481 — Nemotron Nano 3 Omni convert support)

Downloads last month: 12,716

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

16-bit

View +8 variants

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Model tree for mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF

Base model

nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

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Collection including mudler/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-APEX-GGUF

APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 48 items • Updated 10 days ago • 149