Getting Started With Local AI

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ABOUT THE COURSE:

Practical Workshop on Local AI will help you create a complete environment for launching and configuring local AI models, LLM, and agent tools on your own computer. You will learn how to choose models that suit your available hardware, run them through LM Studio, connect them to applications via local API, and create fast, cost-effective, and more private AI-based solutions.

About this Workshop

This training is dedicated to the practical work with local language models and developing a convenient workflow for developers. Instead of being constantly dependent on cloud AI services, you will learn to run models on your own device, control data, manage performance, and select optimal settings for specific tasks.

The center of the workshop is LM Studio: a tool for downloading, launching, and testing local LLM. You will learn how language models work, what tokens are, the context window, inference, model formats, and quantization, then apply this knowledge in practice.

Special attention is given to integration: you will learn to provide access to a local model via an API compatible with OpenAI API, connect it to applications, developer tools, and agent AI workflows.

What You Will Learn to Do

• Understand the basic principles of LLM: tokens, context windows, inference, model limitations, and hardware requirements.

• Select suitable local AI models for tasks, memory volume, CPU type, and GPU.

• Understand model formats and quantization levels to find a balance between quality, speed, and resource consumption.

• Download, configure, and run local models using LM Studio.

• Work with a local API compatible with OpenAI and connect models to your own applications.

• Integrate local LLM into developer tools and agent AI-workflows.

• Optimize model performance for available hardware.

• Evaluate trade-offs between answer quality, generation speed, memory usage, security, and privacy.

• Create practical AI tools that work locally and do not require constant access to cloud services.

Practical Workflow with Local AI

The workshop is structured as a sequential path from understanding the basics to launching your own local AI environment. You will not just be introduced to theory but will go through real setup stages: model selection, downloading, launching, testing, optimization, and connecting to external tools.

1. Basics of Local Language Models

You will understand how modern LLM work and why some models require powerful GPUs while others can be run on more accessible hardware. This will help you make informed decisions when choosing models instead of relying on popularity or size alone.

2. Model Selection and Quantization

You will learn to compare model families, understand differences between formats, and choose the level of quantization. This is particularly important for local usage where response quality, speed, and memory consumption directly depend on the right configuration.

3. Running Models in LM Studio

You will install and configure LM Studio, download a local model, launch it, and test it on practical tasks. You will also learn to adjust generation parameters for more stable and useful results.

4. Local API and Integration

You will set up access to the model via a local API compatible with OpenAI API. This will allow you to use the local LLM in your own applications, code editors, agent tools, and automated workflows.

5. Performance, Security, and Privacy

You will learn how to control memory usage, increase work speed, reduce costs, and address confidentiality issues. Local AI is particularly useful where it is important not to send data to external services.

Who This Course is For

The workshop is designed for developers, engineers, technical specialists, and AI enthusiasts who want to run AI models on their own computer and use them in real projects.

• Developers who want to add local LLM to their applications.

• Engineers working with AI tools and agent workflows.

• Specialists for whom data privacy and independence from cloud APIs are important.

• Those who want to understand what hardware and models are suitable for local AI.

• Beginners in local AI who need a practical and understandable start without unnecessary complexity.

Participant Requirements

• Basic understanding of development and interest in AI tools.

• Preferably some knowledge of agent AI coding or experience using AI assistants for development.

• A computer capable of running local models: a Mac with M-series chips or a PC with a discrete graphics card.

• Willingness to experiment with model settings, performance, and integrations.

What You Will Gain

After completing the workshop, you will have a configured local AI environment and an understanding of how to maintain it in working order. You will be able to independently select models, launch them, connect them to development tools, and use local AI for practical tasks.

• Your own local environment for working with LLM.

• The skill of launching and configuring models in LM Studio.

• An understanding of hardware limitations and optimization methods.

• Experience working with a local API compatible with OpenAI.

• A foundation for creating private, fast, and economical AI tools.

Why Study Local AI

Local AI models provide more control over data, cost, and infrastructure. They allow testing ideas without constant expenses on cloud APIs, handling sensitive information more securely, and creating autonomous tools that remain accessible even without external services.

This workshop is suitable for those who want not just to use AI, but to understand how to launch, configure, and integrate local models into their own workflow.