Instructions to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF",
	filename="nvidia_Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

Ollama
How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
```

Unsloth Studio

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF to start chatting

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

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": "Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

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 Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

Run Hermes

hermes

Docker Model Runner
How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

NVIDIA Nemotron-3-Nano-30B-A3B — IQ4_XS GGUF (RTX 2080 Ti Optimized)

Production-validated IQ4_XS quantization of NVIDIA Nemotron-3-Nano-30B-A3B, benchmarked and tuned for 11GB VRAM GPUs (RTX 2080 Ti / RTX 3060 / etc).

GGUF sourced from bartowski/nvidia_Nemotron-3-Nano-30B-A3B-GGUF. This repo adds real-world benchmarks, production configs, and deployment guides for constrained-VRAM setups.

Why This Repo?

bartowski provides the quants — we provide the deployment playbook for running this model on a single consumer GPU with real benchmark data, not projections.

✅ Validated tool calling (function calls parse correctly)
✅ Validated reasoning (chain-of-thought with <think> tags)
✅ Production Ollama Modelfile included
✅ llama-server launch configs with measured VRAM headroom
✅ Tested across 168 messages in a multi-agent war room

Model Details

Property	Value
Base Model	NVIDIA Nemotron-3-Nano-30B-A3B
Architecture	MoE Hybrid: 23 Mamba-2 + 6 Attention + 128 experts + 1 shared
Total Parameters	31.6B
Active Parameters	~3.2–3.6B (Mixture of Experts)
Quantization	IQ4_XS (imatrix)
File Size	16.8 GB
Format	GGUF
Prompt Format	ChatML (`<\|im_start\|>`)
Context Window	Up to 5120 tested stable on 11GB (hardware dependent)
License	NVIDIA Open Model License

Benchmarks (RTX 2080 Ti, 11GB VRAM)

All benchmarks on: Ryzen 7 3800X (8c/16t) · 32GB DDR4-3600 · RTX 2080 Ti 11GB · Ubuntu 24.04

Throughput

Backend	GPU Layers	KV Cache	Context	Flash Attn	tok/s (single)	tok/s (sustained)
Ollama (Q4_K_M)	28/52 (est.)	default	4096	❌	11.04	—
Ollama (IQ4_XS)	28/52	default	4096	❌	14.49	—
llama-server (IQ4_XS)	28	q8_0	4096	❌	22.85	19.45–19.69
llama-server (IQ4_XS)	28	q8_0	4096	✅	26.25	26.21–26.70

VRAM Usage

Config	VRAM Used	Free	Headroom
Ollama Q4_K_M	10,449 MiB	815 MiB	⚠️ Tight
Ollama IQ4_XS	10,079 MiB	1,185 MiB	✅ Good
llama-server ctx 2048 (×1 slot)	10,631 MiB	633 MiB	✅ Safe
llama-server ctx 3072 (×1 slot)	10,627 MiB	637 MiB	✅ Safe
llama-server ctx 4096 (×1 slot)	10,639 MiB	625 MiB	✅ Production
llama-server ctx 5120 (×1 slot)	10,641 MiB	623 MiB	✅ Safe
llama-server ctx 6144 (×1 slot)	10,947 MiB	317 MiB	⚠️ Tight
llama-server ctx 8192 (×1 slot)	11,085 MiB	179 MiB	❌ OOM risk
llama-server ctx 4096 (×4 slots)	~10,953 MiB	311 MiB	⚠️ Dangerous

Why does context barely affect VRAM? Nemotron's hybrid Mamba-2 architecture uses recurrent state (fixed size) for 46 of 52 layers. Only 6 attention layers need KV cache, so context scaling is nearly flat until ~6K where compute buffers jump.

IQ4_XS vs Q4_K_M (Same Model, Ollama)

Metric	Q4_K_M	IQ4_XS	Delta
Throughput	11.04 tok/s	14.49 tok/s	+31%
VRAM	10,449 MiB	10,079 MiB	−370 MiB
Cold Start	42.7s	23.7s	−44%
Disk Size	24 GB	16.8 GB	−6 GB
Tool Calling	✅	✅	—
Reasoning	✅	✅	—

Quality Validation

Math reasoning: "What's 15% tip on $28.50?" → Correct ($4.28 → $4.275, rounds properly)
Tool calling: OpenAI-format function calls parse correctly
Multi-turn: Sustained coherence over 168-message war room session

Recommended Configurations

Ollama (Easy, Recommended for Most Users)

Create a Modelfile:

FROM ./nvidia_Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf

PARAMETER temperature 0.3
PARAMETER top_k 40
PARAMETER top_p 0.85
PARAMETER num_ctx 4096
PARAMETER num_predict 512
PARAMETER repeat_penalty 1.1

ollama create nemotron-prod -f Modelfile
ollama run nemotron-prod

llama-server (Maximum Performance, +84% over Ollama)

# Production config (recommended for 11GB VRAM)
llama-server \
  --model nvidia_Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf \
  --n-gpu-layers 28 \
  --cache-type-k q8_0 \
  --ctx-size 4096 \
  --parallel 1 \
  --flash-attn on \
  --port 8081

Flash Attention: Build llama.cpp with GGML_CUDA_FA=ON and use --flash-attn on (not auto — auto may fail to enable on split GPU/CPU models). FA gives +35% sustained throughput on this model.

28 GPU layers is the stable ceiling for 11GB VRAM. 29 loads but OOMs during generation. 30+ fails at load.

Context window scaling (measured on RTX 2080 Ti, single slot):

--ctx-size 2048 → 633 MiB free ✅
--ctx-size 3072 → 637 MiB free ✅
--ctx-size 4096 → 625 MiB free ✅ ← Production sweet spot
--ctx-size 5120 → 623 MiB free ✅ (max safe)
--ctx-size 6144 → 317 MiB free ⚠️ (too tight)
--ctx-size 8192 → 179 MiB free ❌ (OOM risk)

Note: The Mamba-2 hybrid architecture means context barely affects VRAM up to ~5K. The cliff happens around 6K where compute buffers resize.

systemd Service (Auto-start on Boot)

[Unit]
Description=Nemotron IQ4_XS llama-server (CUDA)
After=network.target

[Service]
Type=simple
User=your-user
ExecStart=/opt/llama-server/llama-server \
  --model /path/to/nvidia_Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf \
  --n-gpu-layers 28 \
  --cache-type-k q8_0 \
  --ctx-size 4096 \
  --parallel 1 \
  --flash-attn on \
  --port 8081 \
  --host 127.0.0.1
Restart=on-failure
RestartSec=5
Environment=CUDA_VISIBLE_DEVICES=0

[Install]
WantedBy=multi-user.target

OpenAI-Compatible API

Both Ollama and llama-server expose /v1/chat/completions. Drop-in compatible with any OpenAI SDK client:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8081/v1", api_key="none")
response = client.chat.completions.create(
    model="nemotron",
    messages=[{"role": "user", "content": "Hello!"}]
)

GPU Layer Guide

VRAM	GPU Layers	Est. tok/s	Notes
8 GB	20–22	~12	Tight, reduce context
11 GB	28	22–27	Sweet spot (this config)
16 GB	35–40	~28–32	Comfortable headroom
22 GB	52 (all)	~35–40	Full GPU offload 🚀
24 GB	52 (all)	~35–40	Full offload + large context

22GB upgrade note: With 22GB VRAM, all 52 layers fit on GPU — zero CPU offload, maximum throughput. Expected ~2x improvement over 28-layer config.

File Listing

File	Size	Description
`nvidia_Nemotron-3-Nano-30B-A3B-IQ4_XS.gguf`	16.8 GB	IQ4_XS quantized model (imatrix)
`Modelfile`	<1 KB	Production Ollama Modelfile
`README.md`	—	This file

Quantization Source

GGUF quantized by bartowski using llama.cpp release b7423 with imatrix calibration data. See bartowski/nvidia_Nemotron-3-Nano-30B-A3B-GGUF for all available quant sizes.

Credits

Model: NVIDIA — Nemotron-3-Nano-30B-A3B
Quantization: bartowski — IQ4_XS imatrix GGUF
Benchmarking & Deployment: Tinker-Stack — Production validation on RTX 2080 Ti with Disclaw multi-agent war room

Downloads last month: 37

GGUF

Model size

32B params

Architecture

nemotron_h_moe

Hardware compatibility

4-bit

Model tree for Tinker-Stack/Nemotron-3-Nano-30B-A3B-IQ4_XS-GGUF

Base model

nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Quantized

(44)

this model