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[Deploying Large AI Models Locally on Windows] Part 1: Why Choose Windows Platform for Deploying Open Source LLM

#LLM#Ollama#ChatBox#AI191
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The text discusses the advantages and disadvantages of using Windows as a platform for developing and deploying AI large models. It highlights the hardware requirements and preparation phase for running LLM models locally. It also mentions the installation of Ollama and the deployment of the first LLM model. Additionally, it suggests using ChatBox for accessing AI services and provides a link for downloading it.

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part1: Let's talk about the premise first#

ps: You can ask AI if you don't understand any terms.

Advantages of Windows#

Windows is one of the most widely used operating systems globally, with a large user base. Therefore, developing and deploying AI models on the Windows platform can make it easier to reach a wider audience, thereby promoting the popularization and application of AI technology.

The Windows platform provides rich development tools and resources, such as Visual Studio, Python, etc., which makes it easier for developers to create and deploy large AI models. In addition, the Windows platform supports various hardware devices, including GPUs, FPGAs, etc., enabling efficient execution of large AI models on the Windows platform.
Windows platform also has strong security and stability, allowing AI models to run safely on Windows and ensuring their stability and reliability. Furthermore, the Windows platform provides a wide range of applications and services, allowing better integration of AI models with other applications and services, thereby improving their usability and practicality.

In summary, choosing the Windows platform as the development and deployment platform for large AI models can fully leverage the advantages of the Windows platform, improving the development efficiency and application value of AI models.

Disadvantages of Windows#

  • Hardware limitations: Windows platform usually requires higher hardware configurations to run large AI models, which may limit their application on low-end devices.
  • Performance limitations: Windows platform has relatively weak memory management and multi-threading capabilities, which may affect the performance and efficiency of large AI models.
  • Security issues: Windows platform has some security vulnerabilities and risks, which may lead to data leakage and security problems for large AI models.
  • Development tool limitations: Windows platform has relatively fewer development tools and resources, which may limit the development and customization capabilities of large AI models.

In conclusion, as a development and deployment platform for large AI models, the Windows platform has some disadvantages and limitations. Therefore, when choosing the Windows platform, it is necessary to consider factors such as hardware, performance, security, and development tools to ensure the smooth operation and application of AI models.

Why not use Windows Copilot?#

It is difficult for domestic users to use it, you know.

Why not use domestic large models?#

Sure, but today we want to deploy a local model ourselves.

part2: Preparation stage#

What hardware can run it?#

  • Operating System: The latest version of Windows 11.
  • Processor: At least Intel Core i5 or equivalent AMD processor.
  • Memory: At least 8GB RAM, recommended 16GB or more.
  • Storage Space: At least 10GB of free hard disk space, depending on the size of the model.
  • Network: Stable internet connection for downloading models and dependencies.

The advantages of deploying the LLM model locally include:

  • Scalability: Better control over the hardware, trained models, data, and software used to run the service.
  • Performance: Ability to optimize training and inference processes, comply with specific regulations, and improve the performance of LLM.
  • Data Privacy: Better protection of data privacy and prevention of data leakage.
  • Customization: Ability to customize LLM according to specific requirements and build dedicated models.

part3: Installing ollama#

The official documentation for ollama can be found at the following links:
Starting from March 14th, Ollama now supports AMD graphics cards, which means that ollama is the most compatible software for all platforms.

  1. ollama GitHub Repository - Provides the source code and related documentation for the ollama project.
  2. ollama API Documentation - Provides instructions and explanations on how to use the ollama API.
  3. ollama FAQ Documentation - Frequently asked questions, including updates, installation, and other issues.
  4. ollama Official Website - Provides an overview of the ollama project and some basic information.
  5. ollama Usage Tutorial - Provides installation and usage guidelines for ollama.
  6. ollama Library Resources - Provides resources and documentation related to ollama.

These resources can help you understand how to deploy and use ollama, including installation steps, configuration methods, and API usage.

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Deploying the first LLM model#

After installing ollama, go to the models page
https://ollama.com/library
and find the model you want. Simply copy the command.

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You will see a llama icon in your taskbar.
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At this point, your local model has been deployed. You can now use it in the command line, but you might find it uncomfortable. So, let's continue and configure a client to interact with ollama.

Installing ChatBox#

ChatBox supports multiple advanced AI model services globally and is available for Windows, Mac, and Linux. AI improves work efficiency and has received high praise from professionals worldwide.
Download link: https://chatboxai.app/zh

Connecting ollama and ChatBox#

After installing ChatBox, you need to configure the API.

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Ask away!

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With this, Part 1 is complete. In the next part, let's play with Docker.

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