Instructions to use bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/granite-4.2-30b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/granite-4.2-30b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/granite-4.2-30b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/granite-4.2-30b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/granite-4.2-30b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/granite-4.2-30b-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": "bartowski/granite-4.2-30b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/granite-4.2-30b-GGUF:Q4_K_M

Ollama
How to use bartowski/granite-4.2-30b-GGUF with Ollama:
```
ollama run hf.co/bartowski/granite-4.2-30b-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/granite-4.2-30b-GGUF to start chatting

How to use bartowski/granite-4.2-30b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/granite-4.2-30b-GGUF:Q4_K_M

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": "bartowski/granite-4.2-30b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/granite-4.2-30b-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/granite-4.2-30b-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/granite-4.2-30b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/granite-4.2-30b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.2-30b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-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 bartowski/granite-4.2-30b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/granite-4.2-30b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/granite-4.2-30b-GGUF:Q4_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 "bartowski/granite-4.2-30b-GGUF:Q4_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"

Llamacpp imatrix Quantizations of granite-4.2-30b by ibm-granite

Using llama.cpp release b10603 for quantization.

Original model: https://huggingface.co/ibm-granite/granite-4.2-30b

Model details:

Parameter count: 29B
Input support: text
Speculative decoding: no
imatrix: yes - details

How to run

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

Don't know which to choose? Grab Q4_K_M (18.03GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
granite-4.2-30b-bf16.gguf	bf16	58.56GB	true	Full BF16 weights.
granite-4.2-30b-Q8_0.gguf	Q8_0	31.11GB	false	Extremely high quality, generally unneeded but max available quant.
granite-4.2-30b-Q6_K_L.gguf	Q6_K_L	24.71GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
granite-4.2-30b-Q6_K.gguf	Q6_K	24.51GB	false	Very high quality, near perfect, recommended.
granite-4.2-30b-Q5_K_L.gguf	Q5_K_L	21.28GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
granite-4.2-30b-Q5_K_M.gguf	Q5_K_M	21.03GB	false	High quality, recommended.
granite-4.2-30b-Q5_K_S.gguf	Q5_K_S	20.26GB	false	High quality, recommended.
granite-4.2-30b-Q4_1.gguf	Q4_1	18.49GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
granite-4.2-30b-Q4_K_L.gguf	Q4_K_L	18.33GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
granite-4.2-30b-Q4_K_M.gguf	Q4_K_M	18.03GB	false	Good quality, default size for most use cases, recommended.
granite-4.2-30b-Q4_K_S.gguf	Q4_K_S	16.87GB	false	Slightly lower quality with more space savings, recommended.
granite-4.2-30b-IQ4_NL.gguf	IQ4_NL	16.79GB	false	Similar to IQ4_XS, but slightly larger.
granite-4.2-30b-Q4_0.gguf	Q4_0	16.79GB	false	Legacy format, kept for compatibility with older tools.
granite-4.2-30b-IQ4_XS.gguf	IQ4_XS	15.91GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
granite-4.2-30b-Q3_K_XL.gguf	Q3_K_XL	15.74GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
granite-4.2-30b-Q3_K_L.gguf	Q3_K_L	15.38GB	false	Lower quality but usable, good for low RAM availability.
granite-4.2-30b-Q3_K_M.gguf	Q3_K_M	14.36GB	false	Low quality.
granite-4.2-30b-IQ3_M.gguf	IQ3_M	13.31GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
granite-4.2-30b-Q3_K_S.gguf	Q3_K_S	12.96GB	false	Low quality, not recommended.
granite-4.2-30b-IQ3_XS.gguf	IQ3_XS	12.39GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
granite-4.2-30b-IQ3_XXS.gguf	IQ3_XXS	11.69GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
granite-4.2-30b-Q2_K_L.gguf	Q2_K_L	11.52GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
granite-4.2-30b-Q2_K.gguf	Q2_K	11.12GB	false	Very low quality but surprisingly usable.
granite-4.2-30b-IQ2_M.gguf	IQ2_M	10.83GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
granite-4.2-30b-IQ2_S.gguf	IQ2_S	10.12GB	false	Low quality, uses SOTA techniques to be usable.
granite-4.2-30b-IQ2_XS.gguf	IQ2_XS	9.76GB	false	Low quality, uses SOTA techniques to be usable.
granite-4.2-30b-IQ2_XXS.gguf	IQ2_XXS	9.03GB	false	Very low quality, uses SOTA techniques to be usable.

Download a specific file:

hf download bartowski/granite-4.2-30b-GGUF --include "granite-4.2-30b-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

Click to view download instructions

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/granite-4.2-30b-GGUF --include "granite-4.2-30b-Q4_K_M.gguf" --local-dir ./

The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

hf download bartowski/granite-4.2-30b-GGUF --include "granite-4.2-30b-bf16/*" --local-dir ./

You can either specify a new local-dir (granite-4.2-30b-bf16) or download them all in place (./)

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/granite-4.2-30b-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b10603 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

imatrix

All quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs plain prose with tool-calling and reasoning conversations (corpus source data), encoded exactly as this model sees them at inference and processed with --parse-special, so chat-format special tokens contribute to the importance matrix. The corpus rendered for this model is included in this repo: granite-4.2-30b-calibration-v6.txt. The imatrix is available here: granite-4.2-30b-imatrix.gguf.

Calibration render details

{
  "generator": "auto_quant_v2 calibration renderer",
  "recipe": "calibration-v6",
  "model": "granite-4.2-30b",
  "encoder": "chat_template",
  "chunk_size": 512,
  "prose_chunks": 238,
  "tool_chunks": 335,
  "total_chunks": 573,
  "tool_chunk_fraction": 0.585,
  "n_conversations": 137,
  "extension_convs_used": 0,
  "conversation_token_lengths": [
    564,
    1607,
    917,
    1357,
    1139,
    1405,
    2624,
    645,
    1264,
    1198,
    1039,
    2048,
    890,
    992,
    2454,
    1260,
    1095,
    986,
    754,
    686,
    1386,
    1046,
    1427,
    1192,
    1728,
    1524,
    1058,
    750,
    988,
    1454,
    1579,
    812,
    1267,
    1031,
    1046,
    1706,
    1634,
    1169,
    484,
    1909,
    1314,
    1127,
    1401,
    1786,
    1975,
    1334,
    1554,
    775,
    2485,
    1073,
    2687,
    776,
    1014,
    766,
    864,
    705,
    2312,
    756,
    1114,
    1058,
    1257,
    1172,
    958,
    1005,
    911,
    1335,
    915,
    1520,
    1854,
    861,
    331,
    1124,
    3109,
    2900,
    717,
    761,
    1046,
    804,
    1302,
    1097,
    1167,
    824,
    1050,
    1094,
    1274,
    1531,
    1367,
    1436,
    867,
    665,
    2520,
    651,
    1108,
    1625,
    1967,
    1198,
    633,
    1357,
    1077,
    1678,
    1718,
    1760,
    832,
    794,
    1061,
    3021,
    752,
    599,
    788,
    1188,
    1067,
    1290,
    767,
    357,
    320,
    2367,
    759,
    1121,
    1698,
    1742,
    2417,
    2433,
    694,
    975,
    841,
    879,
    1242,
    1031,
    854,
    1387,
    817,
    725,
    1409,
    985,
    909,
    1339,
    1442
  ],
  "warnings": []
}

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?

Click here for details

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Downloads last month: -

GGUF

Model size

29B params

Architecture

granite

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

View +1 variant

Model tree for bartowski/granite-4.2-30b-GGUF

Base model

ibm-granite/granite-4.1-30b-base

Finetuned

ibm-granite/granite-4.2-30b

Quantized

(12)

this model

Collections including bartowski/granite-4.2-30b-GGUF