Instructions to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M",
	filename="Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

LM Studio
Jan

vLLM

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Ollama
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Ollama:
```
ollama run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```

Unsloth Studio

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M 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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M to start chatting

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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": "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

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 "majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M" \
  --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"

Docker Model Runner
How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Docker Model Runner:
```
docker model run hf.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```

Lemonade

How to use majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Run and chat with the model

lemonade run user.Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-Q3_K_M

List all available models

lemonade list

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M / README.md

majentik

docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental

1b73122 verified 6 days ago

preview code

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	---
	license: other
	license_name: nvidia-open-model-license
	license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
	base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
	tags: [nemotron, multimodal, mamba2, moe, quantized, rotorquant, gguf, llama.cpp,
	llama-mtmd, multimodal-via-mmproj]
	library_name: gguf
	pipeline_tag: image-text-to-text
	language: [en]
	datasets: [nvidia/Nemotron-Image-Training-v3]
	inference: false
	---

	> [!TIP]
	> KV-cache quantization without any fork (recommended, 2026): upstream
	> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
	> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
	> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
	> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
	> K and V types symmetric to stay on the fast fused Flash-Attention path.
	> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
	> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
	> which greatly improves low-bit KV quality (opt-out:
	> `LLAMA_ATTN_ROT_DISABLE=1`).
	>
	> The RotorQuant/TurboQuant fork flow below is experimental/legacy: the
	> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
	> is unmaintained relative to mainline. It is NOT required to use this model.
	<!-- kv-upstream-note -->

	# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q3_K_M

	GGUF Q3_K_M quantization of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`) with RotorQuant weight method.

	The `Q3_K_M.gguf` binary in this repo is loaded by `llama.cpp` / `llama-mtmd-cli`.
	For multimodal inference (text + image + audio + video) pair this with the
	multimodal projector: [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

	For the matched-KV stack — RotorQuant weights + RotorQuant KV-cache modifier —
	see [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-RQ-KV`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-RQ-KV).
	For the runtime KV-cache modifier itself (weight-agnostic), see
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant).

	## Quickstart

	```bash
	# 1. Download the GGUF + the multimodal projector
	huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M Q3_K_M.gguf --local-dir ./model
	huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

	# 2. Multimodal inference (text + image + audio + video)
	llama-mtmd-cli \
	-m ./model/Q3_K_M.gguf \
	--mmproj ./mmproj/mmproj-F16.gguf \
	--image cat.jpg \
	-p "Describe this image in detail" \
	--temp 0.6 --top-p 0.95 -n 512

	# 3. Text-only inference (no mmproj needed)
	llama-cli \
	-m ./model/Q3_K_M.gguf \
	-p "What is the capital of France?" \
	--temp 0.6 --top-p 0.95 -n 256

	# Disable extended reasoning (default is on):
	# add `--chat-template-kwargs '{"enable_thinking": false}'`
	```

	> ⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

	## Modality matrix

	\| Modality \| Encoder \| Quantization in this variant \|
	\|---\|---\|---\|
	\| Text \| LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE) \| per the variant suffix \|
	\| Image \| CRADIO v4-H \| BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file) \|
	\| Audio \| Parakeet-TDT-0.6B-v2 \| BF16 (same rationale) \|
	\| Video \| Parakeet-TDT-0.6B-v2 + frame sampler \| BF16 (≤ 2 min, 256 frames @ 2 FPS) \|

	NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal
	MLP projectors in BF16 to preserve multimodal accuracy. We follow that
	convention in every quantized variant we ship.

	## Runtime quirks

	### llama.cpp

	Use `llama-mtmd-cli` for multimodal inference; pass `--mmproj mmproj-F16.gguf`
	(see `majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16`).

	Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or
	use the Metal/CPU paths.

	### Ollama

	Text-only; multimodal is blocked because Ollama doesn't yet support
	the mmproj split-file pattern.

	### Reasoning mode

	`enable_thinking` defaults to `True`. To disable extended reasoning
	(e.g., for latency-sensitive cases), pass `enable_thinking=False`
	to the chat template / generate call. No separate "no-think"
	variant card exists — this is a runtime flag, not a model variant.

	## Quant trade-off (GGUF lane)

	\| Quant \| Approx size \| Use case \| Recommendation \|
	\|---\|---\|---\|---\|
	\| Q2_K \| ~17 GB \| Lossy, low-RAM CPU/edge \| Resource-constrained inference \|
	\| Q3_K_M \| ~19 GB \| Smaller-than-Q4, modest quality drop \| Edge devices with ~16 GB RAM \|
	\| IQ4_XS \| ~16 GB \| Importance-quant 4-bit, smaller than Q4_K_M \| Best size/quality at 4-bit \|
	\| Q4_K_M \| ~23 GB \| Balanced default \| Recommended for most users \|
	\| Q5_K_M \| ~24 GB \| Higher fidelity than Q4 \| Quality-sensitive applications \|
	\| Q6_K \| ~28 GB \| Approaching FP16 quality \| High-fidelity CPU/edge \|
	\| Q8_0 \| ~32 GB \| Near-lossless reference \| Fidelity-critical work \|
	\| MXFP4_MOE \| ~17 GB \| Microscaling FP4 (MoE-aware) \| vLLM / transformers users \|

	(Current variant — Q3_K_M — is bolded.)

	## Variants in this family

	(Showing 56 sibling variants under `majentik/nemotron3-nano-omni-30b-`. The current variant — `RotorQuant-GGUF-Q3_K_M` — is bolded*.)

	\| Variant \| Runtime \| Approx size \| Use case \|
	\|---\|---\|---\|---\|
	\| [mmproj-F16](https://huggingface.co/majentik/nemotron3-nano-omni-30b-mmproj-f16) \| llama-mtmd-cli \| ~1-2 GB \| Multimodal projector (pair with any GGUF) \|
	\| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant) \| runtime modifier \| n/a \| KV-cache root (weight-agnostic) \|
	\| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-IQ4_XS) \| llama.cpp \| ~26 GB \| Lossy 4-bit, low-RAM CPU/edge \|
	\| [RotorQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-MXFP4_MOE) \| llama.cpp \| ~30 GB \| MXFP4 MoE quant \|
	\| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q2_K) \| llama.cpp \| ~18 GB \| Lossy, low-RAM CPU/edge \|
	\| RotorQuant-GGUF-Q3_K_M \| llama.cpp \| ~23 GB \| Smaller 3-bit, CPU-friendly \|
	\| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q4_K_M) \| llama.cpp \| ~33 GB \| Balanced default \|
	\| [RotorQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q5_K_M) \| llama.cpp \| ~40 GB \| Higher fidelity, more RAM \|
	\| [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q8_0) \| llama.cpp \| ~63 GB \| Near-lossless reference \|
	\| [RotorQuant-GGUF-IQ4_XS-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-iq4_xs-rq-kv) \| llama.cpp \| ~26 GB \| IQ4_XS + RotorQuant KV \|
	\| [RotorQuant-GGUF-MXFP4_MOE-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-mxfp4_moe-rq-kv) \| llama.cpp \| ~30 GB \| MXFP4 MoE + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q2_K-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q2_k-rq-kv) \| llama.cpp \| ~18 GB \| Q2_K + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q3_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q3_k_m-rq-kv) \| llama.cpp \| ~23 GB \| Q3_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q4_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q4_k_m-rq-kv) \| llama.cpp \| ~33 GB \| Q4_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q5_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q5_k_m-rq-kv) \| llama.cpp \| ~40 GB \| Q5_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q8_0-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q8_0-rq-kv) \| llama.cpp \| ~63 GB \| Q8_0 + RotorQuant KV \|
	\| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit) \| mlx-lm \| ~9.6 GB \| Apple Silicon, smallest \|
	\| [RotorQuant-MLX-2bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit-rq-kv) \| mlx-lm \| ~9.6 GB \| 2-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit) \| mlx-lm \| ~14 GB \| Apple Silicon, small \|
	\| [RotorQuant-MLX-3bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit-rq-kv) \| mlx-lm \| ~14 GB \| 3-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit) \| mlx-lm \| ~19 GB \| Apple Silicon balanced \|
	\| [RotorQuant-MLX-4bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit-rq-kv) \| mlx-lm \| ~19 GB \| 4-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit) \| mlx-lm \| ~23 GB \| Apple Silicon, higher fidelity \|
	\| [RotorQuant-MLX-5bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit-rq-kv) \| mlx-lm \| ~23 GB \| 5-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit) \| mlx-lm \| ~27 GB \| Apple Silicon, near-lossless \|
	\| [RotorQuant-MLX-6bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit-rq-kv) \| mlx-lm \| ~27 GB \| 6-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit) \| mlx-lm \| ~35 GB \| Apple Silicon reference \|
	\| [RotorQuant-MLX-8bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit-rq-kv) \| mlx-lm \| ~35 GB \| 8-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-MXFP4](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-mxfp4) \| mlx-lm \| ~19 GB \| Apple Silicon MXFP4 \|
	\| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant) \| runtime modifier \| n/a \| KV-cache root (weight-agnostic) \|
	\| [TurboQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-IQ4_XS) \| llama.cpp \| ~26 GB \| Lossy 4-bit, low-RAM CPU/edge \|
	\| [TurboQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-MXFP4_MOE) \| llama.cpp \| ~30 GB \| MXFP4 MoE quant \|
	\| [TurboQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q2_K) \| llama.cpp \| ~18 GB \| Lossy, low-RAM CPU/edge \|
	\| [TurboQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q3_K_M) \| llama.cpp \| ~23 GB \| Smaller 3-bit, CPU-friendly \|
	\| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q4_K_M) \| llama.cpp \| ~33 GB \| Balanced default \|
	\| [TurboQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q5_K_M) \| llama.cpp \| ~40 GB \| Higher fidelity, more RAM \|
	\| [TurboQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q8_0) \| llama.cpp \| ~63 GB \| Near-lossless reference \|
	\| [TurboQuant-GGUF-IQ4_XS-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-iq4_xs-tq-kv) \| llama.cpp \| ~26 GB \| IQ4_XS + TurboQuant KV \|
	\| [TurboQuant-GGUF-MXFP4_MOE-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-mxfp4_moe-tq-kv) \| llama.cpp \| ~30 GB \| MXFP4 MoE + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q2_K-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q2_k-tq-kv) \| llama.cpp \| ~18 GB \| Q2_K + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q3_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q3_k_m-tq-kv) \| llama.cpp \| ~23 GB \| Q3_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q4_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q4_k_m-tq-kv) \| llama.cpp \| ~33 GB \| Q4_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q5_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q5_k_m-tq-kv) \| llama.cpp \| ~40 GB \| Q5_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q8_0-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q8_0-tq-kv) \| llama.cpp \| ~63 GB \| Q8_0 + TurboQuant KV \|
	\| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit) \| mlx-lm \| ~9.6 GB \| Apple Silicon, smallest \|
	\| [TurboQuant-MLX-2bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit-tq-kv) \| mlx-lm \| ~9.6 GB \| 2-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit) \| mlx-lm \| ~14 GB \| Apple Silicon, small \|
	\| [TurboQuant-MLX-3bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit-tq-kv) \| mlx-lm \| ~14 GB \| 3-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit) \| mlx-lm \| ~19 GB \| Apple Silicon balanced \|
	\| [TurboQuant-MLX-4bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit-tq-kv) \| mlx-lm \| ~19 GB \| 4-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit) \| mlx-lm \| ~23 GB \| Apple Silicon, higher fidelity \|
	\| [TurboQuant-MLX-5bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit-tq-kv) \| mlx-lm \| ~23 GB \| 5-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit) \| mlx-lm \| ~27 GB \| Apple Silicon, near-lossless \|
	\| [TurboQuant-MLX-6bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit-tq-kv) \| mlx-lm \| ~27 GB \| 6-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit) \| mlx-lm \| ~35 GB \| Apple Silicon reference \|
	\| [TurboQuant-MLX-8bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit-tq-kv) \| mlx-lm \| ~35 GB \| 8-bit + TurboQuant KV \|

	---
	license: other
	license_name: nvidia-open-model-license
	license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
	base_model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
	tags: [nemotron, multimodal, mamba2, moe, quantized, rotorquant, gguf, llama.cpp,
	llama-mtmd, multimodal-via-mmproj]
	library_name: gguf
	pipeline_tag: image-text-to-text
	language: [en]
	datasets: [nvidia/Nemotron-Image-Training-v3]
	inference: false
	---

	> [!TIP]
	> KV-cache quantization without any fork (recommended, 2026): upstream
	> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
	> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
	> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
	> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
	> K and V types symmetric to stay on the fast fused Flash-Attention path.
	> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
	> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
	> which greatly improves low-bit KV quality (opt-out:
	> `LLAMA_ATTN_ROT_DISABLE=1`).
	>
	> The RotorQuant/TurboQuant fork flow below is experimental/legacy: the
	> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
	> is unmaintained relative to mainline. It is NOT required to use this model.
	<!-- kv-upstream-note -->

	# Nemotron-3-Nano-Omni-30B-A3B-Reasoning - RotorQuant GGUF Q3_K_M

	GGUF Q3_K_M quantization of `Nemotron-3-Nano-Omni-30B-A3B-Reasoning` (`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`) with RotorQuant weight method.

	The `Q3_K_M.gguf` binary in this repo is loaded by `llama.cpp` / `llama-mtmd-cli`.
	For multimodal inference (text + image + audio + video) pair this with the
	multimodal projector: [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

	For the matched-KV stack — RotorQuant weights + RotorQuant KV-cache modifier —
	see [`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-RQ-KV`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M-RQ-KV).
	For the runtime KV-cache modifier itself (weight-agnostic), see
	[`majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant`](https://huggingface.co/majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant).

	## Quickstart

	```bash
	# 1. Download the GGUF + the multimodal projector
	huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-RotorQuant-GGUF-Q3_K_M Q3_K_M.gguf --local-dir ./model
	huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

	# 2. Multimodal inference (text + image + audio + video)
	llama-mtmd-cli \
	-m ./model/Q3_K_M.gguf \
	--mmproj ./mmproj/mmproj-F16.gguf \
	--image cat.jpg \
	-p "Describe this image in detail" \
	--temp 0.6 --top-p 0.95 -n 512

	# 3. Text-only inference (no mmproj needed)
	llama-cli \
	-m ./model/Q3_K_M.gguf \
	-p "What is the capital of France?" \
	--temp 0.6 --top-p 0.95 -n 256

	# Disable extended reasoning (default is on):
	# add `--chat-template-kwargs '{"enable_thinking": false}'`
	```

	> ⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

	## Modality matrix

	\| Modality \| Encoder \| Quantization in this variant \|
	\|---\|---\|---\|
	\| Text \| LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE) \| per the variant suffix \|
	\| Image \| CRADIO v4-H \| BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file) \|
	\| Audio \| Parakeet-TDT-0.6B-v2 \| BF16 (same rationale) \|
	\| Video \| Parakeet-TDT-0.6B-v2 + frame sampler \| BF16 (≤ 2 min, 256 frames @ 2 FPS) \|

	NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal
	MLP projectors in BF16 to preserve multimodal accuracy. We follow that
	convention in every quantized variant we ship.

	## Runtime quirks

	### llama.cpp

	Use `llama-mtmd-cli` for multimodal inference; pass `--mmproj mmproj-F16.gguf`
	(see `majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16`).

	Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or
	use the Metal/CPU paths.

	### Ollama

	Text-only; multimodal is blocked because Ollama doesn't yet support
	the mmproj split-file pattern.

	### Reasoning mode

	`enable_thinking` defaults to `True`. To disable extended reasoning
	(e.g., for latency-sensitive cases), pass `enable_thinking=False`
	to the chat template / generate call. No separate "no-think"
	variant card exists — this is a runtime flag, not a model variant.

	## Quant trade-off (GGUF lane)

	\| Quant \| Approx size \| Use case \| Recommendation \|
	\|---\|---\|---\|---\|
	\| Q2_K \| ~17 GB \| Lossy, low-RAM CPU/edge \| Resource-constrained inference \|
	\| Q3_K_M \| ~19 GB \| Smaller-than-Q4, modest quality drop \| Edge devices with ~16 GB RAM \|
	\| IQ4_XS \| ~16 GB \| Importance-quant 4-bit, smaller than Q4_K_M \| Best size/quality at 4-bit \|
	\| Q4_K_M \| ~23 GB \| Balanced default \| Recommended for most users \|
	\| Q5_K_M \| ~24 GB \| Higher fidelity than Q4 \| Quality-sensitive applications \|
	\| Q6_K \| ~28 GB \| Approaching FP16 quality \| High-fidelity CPU/edge \|
	\| Q8_0 \| ~32 GB \| Near-lossless reference \| Fidelity-critical work \|
	\| MXFP4_MOE \| ~17 GB \| Microscaling FP4 (MoE-aware) \| vLLM / transformers users \|

	(Current variant — Q3_K_M — is bolded.)

	## Variants in this family

	(Showing 56 sibling variants under `majentik/nemotron3-nano-omni-30b-`. The current variant — `RotorQuant-GGUF-Q3_K_M` — is bolded*.)

	\| Variant \| Runtime \| Approx size \| Use case \|
	\|---\|---\|---\|---\|
	\| [mmproj-F16](https://huggingface.co/majentik/nemotron3-nano-omni-30b-mmproj-f16) \| llama-mtmd-cli \| ~1-2 GB \| Multimodal projector (pair with any GGUF) \|
	\| [RotorQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant) \| runtime modifier \| n/a \| KV-cache root (weight-agnostic) \|
	\| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-IQ4_XS) \| llama.cpp \| ~26 GB \| Lossy 4-bit, low-RAM CPU/edge \|
	\| [RotorQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-MXFP4_MOE) \| llama.cpp \| ~30 GB \| MXFP4 MoE quant \|
	\| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q2_K) \| llama.cpp \| ~18 GB \| Lossy, low-RAM CPU/edge \|
	\| RotorQuant-GGUF-Q3_K_M \| llama.cpp \| ~23 GB \| Smaller 3-bit, CPU-friendly \|
	\| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q4_K_M) \| llama.cpp \| ~33 GB \| Balanced default \|
	\| [RotorQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q5_K_M) \| llama.cpp \| ~40 GB \| Higher fidelity, more RAM \|
	\| [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-Q8_0) \| llama.cpp \| ~63 GB \| Near-lossless reference \|
	\| [RotorQuant-GGUF-IQ4_XS-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-iq4_xs-rq-kv) \| llama.cpp \| ~26 GB \| IQ4_XS + RotorQuant KV \|
	\| [RotorQuant-GGUF-MXFP4_MOE-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-mxfp4_moe-rq-kv) \| llama.cpp \| ~30 GB \| MXFP4 MoE + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q2_K-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q2_k-rq-kv) \| llama.cpp \| ~18 GB \| Q2_K + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q3_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q3_k_m-rq-kv) \| llama.cpp \| ~23 GB \| Q3_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q4_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q4_k_m-rq-kv) \| llama.cpp \| ~33 GB \| Q4_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q5_K_M-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q5_k_m-rq-kv) \| llama.cpp \| ~40 GB \| Q5_K_M + RotorQuant KV \|
	\| [RotorQuant-GGUF-Q8_0-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-gguf-q8_0-rq-kv) \| llama.cpp \| ~63 GB \| Q8_0 + RotorQuant KV \|
	\| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit) \| mlx-lm \| ~9.6 GB \| Apple Silicon, smallest \|
	\| [RotorQuant-MLX-2bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-2bit-rq-kv) \| mlx-lm \| ~9.6 GB \| 2-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit) \| mlx-lm \| ~14 GB \| Apple Silicon, small \|
	\| [RotorQuant-MLX-3bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-3bit-rq-kv) \| mlx-lm \| ~14 GB \| 3-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit) \| mlx-lm \| ~19 GB \| Apple Silicon balanced \|
	\| [RotorQuant-MLX-4bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-4bit-rq-kv) \| mlx-lm \| ~19 GB \| 4-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit) \| mlx-lm \| ~23 GB \| Apple Silicon, higher fidelity \|
	\| [RotorQuant-MLX-5bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-5bit-rq-kv) \| mlx-lm \| ~23 GB \| 5-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit) \| mlx-lm \| ~27 GB \| Apple Silicon, near-lossless \|
	\| [RotorQuant-MLX-6bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-6bit-rq-kv) \| mlx-lm \| ~27 GB \| 6-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit) \| mlx-lm \| ~35 GB \| Apple Silicon reference \|
	\| [RotorQuant-MLX-8bit-RQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-8bit-rq-kv) \| mlx-lm \| ~35 GB \| 8-bit + RotorQuant KV \|
	\| [RotorQuant-MLX-MXFP4](https://huggingface.co/majentik/nemotron3-nano-omni-30b-rotorquant-mlx-mxfp4) \| mlx-lm \| ~19 GB \| Apple Silicon MXFP4 \|
	\| [TurboQuant](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant) \| runtime modifier \| n/a \| KV-cache root (weight-agnostic) \|
	\| [TurboQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-IQ4_XS) \| llama.cpp \| ~26 GB \| Lossy 4-bit, low-RAM CPU/edge \|
	\| [TurboQuant-GGUF-MXFP4_MOE](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-MXFP4_MOE) \| llama.cpp \| ~30 GB \| MXFP4 MoE quant \|
	\| [TurboQuant-GGUF-Q2_K](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q2_K) \| llama.cpp \| ~18 GB \| Lossy, low-RAM CPU/edge \|
	\| [TurboQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q3_K_M) \| llama.cpp \| ~23 GB \| Smaller 3-bit, CPU-friendly \|
	\| [TurboQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q4_K_M) \| llama.cpp \| ~33 GB \| Balanced default \|
	\| [TurboQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q5_K_M) \| llama.cpp \| ~40 GB \| Higher fidelity, more RAM \|
	\| [TurboQuant-GGUF-Q8_0](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-Q8_0) \| llama.cpp \| ~63 GB \| Near-lossless reference \|
	\| [TurboQuant-GGUF-IQ4_XS-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-iq4_xs-tq-kv) \| llama.cpp \| ~26 GB \| IQ4_XS + TurboQuant KV \|
	\| [TurboQuant-GGUF-MXFP4_MOE-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-mxfp4_moe-tq-kv) \| llama.cpp \| ~30 GB \| MXFP4 MoE + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q2_K-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q2_k-tq-kv) \| llama.cpp \| ~18 GB \| Q2_K + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q3_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q3_k_m-tq-kv) \| llama.cpp \| ~23 GB \| Q3_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q4_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q4_k_m-tq-kv) \| llama.cpp \| ~33 GB \| Q4_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q5_K_M-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q5_k_m-tq-kv) \| llama.cpp \| ~40 GB \| Q5_K_M + TurboQuant KV \|
	\| [TurboQuant-GGUF-Q8_0-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-gguf-q8_0-tq-kv) \| llama.cpp \| ~63 GB \| Q8_0 + TurboQuant KV \|
	\| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit) \| mlx-lm \| ~9.6 GB \| Apple Silicon, smallest \|
	\| [TurboQuant-MLX-2bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-2bit-tq-kv) \| mlx-lm \| ~9.6 GB \| 2-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-3bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit) \| mlx-lm \| ~14 GB \| Apple Silicon, small \|
	\| [TurboQuant-MLX-3bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-3bit-tq-kv) \| mlx-lm \| ~14 GB \| 3-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit) \| mlx-lm \| ~19 GB \| Apple Silicon balanced \|
	\| [TurboQuant-MLX-4bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-4bit-tq-kv) \| mlx-lm \| ~19 GB \| 4-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-5bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit) \| mlx-lm \| ~23 GB \| Apple Silicon, higher fidelity \|
	\| [TurboQuant-MLX-5bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-5bit-tq-kv) \| mlx-lm \| ~23 GB \| 5-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-6bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit) \| mlx-lm \| ~27 GB \| Apple Silicon, near-lossless \|
	\| [TurboQuant-MLX-6bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-6bit-tq-kv) \| mlx-lm \| ~27 GB \| 6-bit + TurboQuant KV \|
	\| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit) \| mlx-lm \| ~35 GB \| Apple Silicon reference \|
	\| [TurboQuant-MLX-8bit-TQ-KV](https://huggingface.co/majentik/nemotron3-nano-omni-30b-turboquant-mlx-8bit-tq-kv) \| mlx-lm \| ~35 GB \| 8-bit + TurboQuant KV \|