Instructions to use SandLogicTechnologies/granite-4.1-8b-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 SandLogicTechnologies/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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

Use Docker

docker model run hf.co/SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/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 "SandLogicTechnologies/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": "SandLogicTechnologies/granite-4.1-8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

Ollama
How to use SandLogicTechnologies/granite-4.1-8b-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
```

Unsloth Studio

How to use SandLogicTechnologies/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 SandLogicTechnologies/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 SandLogicTechnologies/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 SandLogicTechnologies/granite-4.1-8b-GGUF to start chatting

How to use SandLogicTechnologies/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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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": "SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use SandLogicTechnologies/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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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 "SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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 SandLogicTechnologies/granite-4.1-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M
```

Lemonade

How to use SandLogicTechnologies/granite-4.1-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

Run and chat with the model

lemonade run user.granite-4.1-8b-GGUF-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use SandLogicTechnologies/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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_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 SandLogicTechnologies/granite-4.1-8b-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat

Granite-4.1-8B

Granite-4.1-8B is an instruction-tuned large language model designed for conversational AI, reasoning, coding assistance, and structured text generation. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

The quantized formats significantly reduce memory requirements while maintaining strong instruction-following and reasoning performance, enabling practical deployment across consumer hardware and edge environments.

Model Overview

Model Name: Granite-4.1-8B
Base Model: ibm-granite/granite-4.1-8b
Architecture: Decoder-only Transformer
Parameter Count: 8 Billion
Modalities: Text
Primary Languages: English
Developer: IBM Granite
License: Apache 2.0

Quantization Formats

This repository provides various GGUF quantized versions of the Granite-4.1-8B model, optimized for efficient local inference using llama.cpp. Below are the details of the available I-Matrix (IQ) formats.

IQ3_M

Size reduction of approx 76.74% (3.81 GB) compared to 16-bit (16.38 GB)
Aggressive 3-bit quantization optimized for maximum memory efficiency
Suitable for CPU-only inference and low-memory deployment environments
Maintains lightweight conversational and instruction-following capability
Output quality may reduce on complex reasoning, coding, and long-context tasks

IQ4_NL

Size reduction of approx 70.94% (4.76 GB) compared to 16-bit (16.38 GB)
Advanced 4-bit non-linear quantization designed to better preserve output quality
More suitable for structured reasoning, coding assistance, and analytical tasks
Typically provides stronger consistency compared to lower-bit formats
Slightly increased computational overhead during inference

IQ4_XS

Size reduction of approx 72.34% (4.53 GB) compared to 16-bit (16.38 GB)
Balanced 4-bit quantization focused on efficiency and stable inference performance
Good trade-off between model size, speed, and response quality
Suitable for conversational AI, summarization, and general-purpose local deployment
Maintains reliable generation behavior across most practical workloads

Training Background (Original Model)

Granite-4.1-8B is trained with an emphasis on instruction comprehension, reasoning performance, and reliable text generation across a wide variety of tasks.

Pretraining

Large-scale language pretraining across diverse textual datasets
Focus on contextual understanding and robust language representation
Optimized for downstream conversational and reasoning workloads

Instruction Tuning

Refined using instruction-following datasets and conversational objectives
Enhanced for structured responses and multi-step reasoning
Improved consistency for coding, analysis, and text generation tasks

Key Capabilities

Instruction Following Handles diverse prompts and produces structured, context-aware responses.
Reasoning and Analysis Performs well on multi-step logical and analytical tasks.
Coding Assistance Supports code generation, explanation, and debugging workflows.
Efficient Local Deployment Quantized variants enable practical offline inference on consumer hardware.
Flexible Text Generation Suitable for summarization, Q&A, conversational AI, and structured outputs.

Usage Example

Using llama.cpp

./llama-cli \
  -m SandlogicTechnologies/granite-4.1-8b_IQ4_NL.gguf \
  -p "Explain the concept of knowledge distillation in detail"

Recommended Usecases

Conversational AI Systems Build local assistants and chat applications without cloud dependencies.
Coding and Development Workflows Support debugging, code explanation, and lightweight programming assistance.
Reasoning and Analysis Tasks Generate structured outputs for analytical and multi-step problem-solving tasks.
Research and Experimentation Evaluate prompts, workflows, and local inference strategies.

Acknowledgments

These quantized models are based on the original work by the IBM Granite development team.

Special thanks to:

The IBM Granite team for developing and releasing the Granite-4.1-8B model.
*Georgi Gerganov- and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

Contact

For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/

Downloads last month: 5

GGUF

Model size

9B params

Architecture

granite

Hardware compatibility

3-bit

4-bit

Model tree for SandLogicTechnologies/granite-4.1-8b-GGUF

Base model

ibm-granite/granite-4.1-8b

Quantized

(76)

this model