Instructions to use batiai/Granite-4.1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/Granite-4.1-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Granite-4.1-8B-GGUF",
	filename="ibm-granite-granite-4.1-8b-IQ3_XXS.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Granite-4.1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/batiai/Granite-4.1-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/Granite-4.1-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/Granite-4.1-8B-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": "batiai/Granite-4.1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/Granite-4.1-8B-GGUF:Q4_K_M

Ollama
How to use batiai/Granite-4.1-8B-GGUF with Ollama:
```
ollama run hf.co/batiai/Granite-4.1-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/Granite-4.1-8B-GGUF to start chatting

How to use batiai/Granite-4.1-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Granite-4.1-8B-GGUF:Q4_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": "batiai/Granite-4.1-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-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 batiai/Granite-4.1-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use batiai/Granite-4.1-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf batiai/Granite-4.1-8B-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 "batiai/Granite-4.1-8B-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"

Docker Model Runner
How to use batiai/Granite-4.1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Granite-4.1-8B-GGUF:Q4_K_M
```

Lemonade

How to use batiai/Granite-4.1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Granite-4.1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Granite-4.1-8B-GGUF-Q4_K_M

List all available models

lemonade list

IBM Granite 4.1 8B GGUF — Quantized by BatiAI

Quantizations of IBM Granite 4.1 8B (Dense, Apache 2.0, enterprise-grade) for on-device AI on Mac. Built and verified by BatiAI for BatiFlow.

Why Granite 4.1 8B?

8B Dense beats 32B-A9B MoE — IBM's tighter post-training pipeline matches/exceeds Granite 4.0-H-Small (4× larger MoE) across IFEval, AlpacaEval, MMLU-Pro, BBH, GSM8K, BFCL V3, MBPP+
15T tokens training, 512K context extension
Strong tool calling (BFCL V3: 68.3) and instruction following (ArenaHard: 69.0)
Apache 2.0 — true commercial-friendly, no use restrictions
Released April 29, 2026

Quick Start

ollama pull batiai/granite4.1:q4

Available Quantizations

Quant	Size	VRAM target	Recommended For
Q3_K_M	~4.5GB	~6GB	8GB+ Mac
Q4_K_M	~5GB	~7GB	16GB Mac (recommended)
Q5_K_M	~6GB	~8GB	16GB+ Mac (higher quality)
Q6_K	~7GB	~9GB	24GB+ Mac (max quality)

RAM Requirements

Your Mac RAM	Q3 (4.5GB)	Q4 (5GB)	Q5 (6GB)	Q6 (7GB)
8GB	✅ Tight	⚠️	❌	❌
16GB	✅	✅ Recommended	✅	⚠️
24GB+	✅	✅	✅	✅

Why an 8B Dense?

Granite 4.1's 8B Dense model is built for enterprise: predictable latency, simple deployment, no MoE routing overhead. IBM's improved post-training (SFT + RL alignment) yields tool-calling and chat quality competitive with much larger models.

Granite 4.1 vs Other 8B Models

Model	Architecture	Tool Call (BFCL)	License
Granite 4.1 8B	Dense	68.3	Apache 2.0
Qwen 3.5 9B	Dense	—	Apache 2.0
Llama 3.3 8B	Dense	~63	Llama License

Why BatiAI Quantization?

	BatiAI	Third-party
Source	Official IBM weights	Re-quantized GGUFs
imatrix	✅ wikitext-2 200 chunks	Standard
Tool Calling	✅ Verified	Often untested
BatiAI signed	✅	—

Technical Details

Original Model: ibm-granite/granite-4.1-8b
Architecture: Dense decoder-only, 8B params
Training: 15T tokens, multi-stage with 512K long-context extension
License: Apache 2.0
Quantized with: llama.cpp + imatrix
Quantized by: BatiAI

About BatiFlow

BatiFlow — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited.

License

Quantized from ibm-granite/granite-4.1-8b. License: Apache 2.0.

Benchmarks

Machine	Quant	Cold start	Prompt eval	Token gen	Tested
Mac mini M4 16GB	IQ3_XXS	2.156s	45.74 t/s	6.66 t/s	2026-05-03
Mac mini M4 16GB	IQ4_XS	5.439s	108.35 t/s	15.55 t/s	2026-05-03
Mac mini M4 16GB	Q3_K_M	4.97s	90.12 t/s	12.84 t/s	2026-05-03
Mac mini M4 16GB	Q4_K_M	5.698s	98.99 t/s	14.26 t/s	2026-05-03
Mac mini M4 16GB	Q5_K_M	6.517s	84.44 t/s	12.21 t/s	2026-05-03
Mac mini M4 16GB	Q6_K	6.964s	73.76 t/s	10.49 t/s	2026-05-03
MacBook Pro M4 Max 128GB	IQ3_XXS	2.265s	514.87 t/s	75.08 t/s	2026-05-03
MacBook Pro M4 Max 128GB	IQ4_XS	3.44s	542.74 t/s	77.79 t/s	2026-05-03
MacBook Pro M4 Max 128GB	Q3_K_M	2.186s	420.42 t/s	60.75 t/s	2026-05-03
MacBook Pro M4 Max 128GB	Q4_K_M	2.184s	529.1 t/s	77.44 t/s	2026-05-03
MacBook Pro M4 Max 128GB	Q5_K_M	2.18s	410.75 t/s	57.52 t/s	2026-05-03
MacBook Pro M4 Max 128GB	Q6_K	3.72s	424.95 t/s	60.27 t/s	2026-05-03

Downloads last month: 77

GGUF

Model size

9B params

Architecture

granite

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

Model tree for batiai/Granite-4.1-8B-GGUF

Base model

ibm-granite/granite-4.1-8b

Quantized

(51)

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