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  ollama

Ollama for Intel Core Ultra - Complete Guide

This repository provides a comprehensive guide and automated scripts to run Ollama with accelerated performance using Intel Core Ultra processors (formerly Meteor Lake) on Windows.

Overview

Intel Core Ultra processors integrate a Neural Processing Unit (NPU) and an Integrated Graphics Processing Unit (iGPU), significantly accelerating local Large Language Model (LLM) inference. This project simplifies the setup process, enabling Ollama to leverage these hardware accelerators seamlessly.

System Requirements

  • Processor: Intel Core Ultra 5, 7, or 9 series
  • Operating System: Windows 10/11 (22H2 or later recommended)
  • Memory: 16GB RAM minimum (32GB recommended)
  • Storage: 20GB+ free disk space
  • Software:
    • Microsoft Visual Studio Build Tools 2022
    • Miniforge (Conda)
    • Intel oneAPI Base Toolkit
    • Latest Intel Graphics Drivers

Installation Guide

Automated Installation (Recommended)

These automated scripts simplify the entire setup process:

  1. Download All Files:

    • prerequisites.bat
    • install.bat
    • start.bat
    • master_install.bat

  2. Run master_install.bat as Administrator:

    • This script installs all prerequisites:

      • Visual Studio Build Tools
      • Miniforge (Conda)
      • Intel oneAPI Base Toolkit
      • Configures Environment Variables

      Important: After running, the script will prompt you to restart your computer. Please reboot at this stage.

  3. Run install.bat as Administrator (After Reboot):

    • This script performs the core setup:

      • Creates Conda Environment
      • Installs IPEX-LLM dependencies
      • Initializes Ollama

  4. Run start.bat to Launch Ollama:

    • This script activates Conda environment and starts the Ollama server.

Manual Installation (If Automated Fails)

  1. Install Visual Studio Build Tools:

  2. Install Miniforge:

  3. Install Intel oneAPI Base Toolkit:

  4. Update Intel Graphics Drivers:

  5. Configure Environment Variables:

    • Open System Environment Variables (Control Panel -> System and Security -> System -> Advanced system settings -> Environment Variables)

    • Verify the PATH variable includes the following entries:

      • C:\Users\%USERNAME%\Miniforge3
      • C:\Users\%USERNAME%\Miniforge3\Scripts
      • C:\Program Files (x86)\Intel\oneAPI\compiler\latest\windows\bin

  6. Create Conda Environment:

    • Open PowerShell as Administrator
    conda create -n ollama-intel python=3.11
conda activate ollama-intel

  7. Install IPEX-LLM Dependencies:

    conda activate ollama-intel
pip install sympy==1.12
pip install --pre "ipex-llm[cpp]" --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/

  8. Initialize Ollama:

    mkdir $env:USERPROFILE\ollama-intel
cd $env:USERPROFILE\ollama-intel
init-ollama.bat

  9. Set Execution Policy:

    Set-ExecutionPolicy RemoteSigned -Scope CurrentUser

Running Ollama

  1. Start Ollama:

    • Run start.bat from the repository directory as an administrator.

  2. Access Ollama:

    • Open your web browser and navigate to http://localhost:11434.

Verification

  1. Verify Intel GPU Detection:

    • Run the following command in PowerShell:

      sycl-ls | Select-String "Level-Zero"
      • The output should show your Intel GPU.

  2. Run Ollama Doctor:

    • This will run diagnostics on the setup.

      ollama doctor

Troubleshooting

  • GPU Not Detected:
    • Verify the following:
      • Intel GPU drivers are up-to-date.
      • Intel oneAPI Base Toolkit is correctly installed.
      • Environment variables are correctly set.
      • The correct version of IPEX-LLM is installed.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Intel for developing IPEX-LLM and optimizing performance on Intel hardware.
  • The Ollama team for creating an accessible and powerful local LLM platform.
  • All contributors who have helped refine this guide and automated scripts.

Ollama

Get up and running with large language models.

macOS

Download

Windows

Download

Linux

curl -fsSL https://ollama.com/install.sh | sh

Manual install instructions

Docker

The official Ollama Docker image ollama/ollama is available on Docker Hub.

Libraries

Community

Quickstart

To run and chat with Llama 3.2:

ollama run llama3.2

Model library

Ollama supports a list of models available on ollama.com/library

Here are some example models that can be downloaded:

Model Parameters Size Download
Gemma 3 1B 815MB ollama run gemma3:1b
Gemma 3 4B 3.3GB ollama run gemma3
Gemma 3 12B 8.1GB ollama run gemma3:12b
Gemma 3 27B 17GB ollama run gemma3:27b
QwQ 32B 20GB ollama run qwq
DeepSeek-R1 7B 4.7GB ollama run deepseek-r1
DeepSeek-R1 671B 404GB ollama run deepseek-r1:671b
Llama 3.3 70B 43GB ollama run llama3.3
Llama 3.2 3B 2.0GB ollama run llama3.2
Llama 3.2 1B 1.3GB ollama run llama3.2:1b
Llama 3.2 Vision 11B 7.9GB ollama run llama3.2-vision
Llama 3.2 Vision 90B 55GB ollama run llama3.2-vision:90b
Llama 3.1 8B 4.7GB ollama run llama3.1
Llama 3.1 405B 231GB ollama run llama3.1:405b
Phi 4 14B 9.1GB ollama run phi4
Phi 4 Mini 3.8B 2.5GB ollama run phi4-mini
Mistral 7B 4.1GB ollama run mistral
Moondream 2 1.4B 829MB ollama run moondream
Neural Chat 7B 4.1GB ollama run neural-chat
Starling 7B 4.1GB ollama run starling-lm
Code Llama 7B 3.8GB ollama run codellama
Llama 2 Uncensored 7B 3.8GB ollama run llama2-uncensored
LLaVA 7B 4.5GB ollama run llava
Granite-3.2 8B 4.9GB ollama run granite3.2

Note

You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

Customize a model

Import from GGUF

Ollama supports importing GGUF models in the Modelfile:

  1. Create a file named Modelfile, with a FROM instruction with the local filepath to the model you want to import.

    FROM ./vicuna-33b.Q4_0.gguf

  2. Create the model in Ollama

    ollama create example -f Modelfile

  3. Run the model

    ollama run example

Import from Safetensors

See the guide on importing models for more information.

Customize a prompt

Models from the Ollama library can be customized with a prompt. For example, to customize the llama3.2 model:

ollama pull llama3.2

Create a Modelfile:

FROM llama3.2

# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1

# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""

Next, create and run the model:

ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.

For more information on working with a Modelfile, see the Modelfile documentation.

CLI Reference

Create a model

ollama create is used to create a model from a Modelfile.

ollama create mymodel -f ./Modelfile

Pull a model

ollama pull llama3.2

This command can also be used to update a local model. Only the diff will be pulled.

Remove a model

ollama rm llama3.2

Copy a model

ollama cp llama3.2 my-model

Multiline input

For multiline input, you can wrap text with """:

>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.

Multimodal models

ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"

Output: The image features a yellow smiley face, which is likely the central focus of the picture.

Pass the prompt as an argument

ollama run llama3.2 "Summarize this file: $(cat README.md)"

Output: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.

Show model information

ollama show llama3.2

List models on your computer

ollama list

List which models are currently loaded

ollama ps

Stop a model which is currently running

ollama stop llama3.2

Start Ollama

ollama serve is used when you want to start ollama without running the desktop application.

Building

See the developer guide

Running local builds

Next, start the server:

./ollama serve

Finally, in a separate shell, run a model:

./ollama run llama3.2

REST API

Ollama has a REST API for running and managing models.

Generate a response

curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt":"Why is the sky blue?"
}'

Chat with a model

curl http://localhost:11434/api/chat -d '{
  "model": "llama3.2",
  "messages": [
    { "role": "user", "content": "why is the sky blue?" }
  ]
}'

See the API documentation for all endpoints.

Community Integrations

Web & Desktop

Cloud

Terminal

Apple Vision Pro

  • SwiftChat (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
  • Enchanted

Database

  • pgai - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
  • MindsDB (Connects Ollama models with nearly 200 data platforms and apps)
  • chromem-go with example
  • Kangaroo (AI-powered SQL client and admin tool for popular databases)

Package managers

Libraries

Mobile

  • SwiftChat (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
  • Enchanted
  • Maid
  • Ollama App (Modern and easy-to-use multi-platform client for Ollama)
  • ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
  • Ollama Android Chat (No need for Termux, start the Ollama service with one click on an Android device)
  • Reins (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)

Extensions & Plugins

Supported backends

  • llama.cpp project founded by Georgi Gerganov.

Observability

  • Opik is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
  • Lunary is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
  • OpenLIT is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
  • HoneyHive is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
  • Langfuse is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
  • MLflow Tracing is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.

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Ollama for Intel Core Ultra NPU/iGPU

ollama.com

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