Hands-On Local LLMs: Run and Build with AI on Your Own Hardware - C2

Downloading a local model and getting it to answer a prompt is easy.

Choosing the right model, running it efficiently on your hardware and integrating it into a reliable application is much harder.

In this live three-hour workshop, you’ll work through the complete local LLM stack, from model and hardware selection to quantization, inference, application integration, evaluation and LoRA.

You’ll explore tools including **Ollama, llama.cpp, MLX and vLLM** while learning how to make practical decisions around privacy, cost, latency, licensing, performance and reliability.

The goal is to help you confidently answer

**When should we run an LLM locally, what should we run, and how do we make it reliable enough for a real application?**

Why Attend?

Local LLMs can keep sensitive data inside your environment, reduce dependence on hosted APIs, support offline applications and give you greater control over cost and infrastructure.

But those advantages depend on choosing the right model, hardware and serving stack.

This workshop will help you move beyond experimentation and understand the engineering decisions behind a dependable local AI system.

What You’ll Learn

• Compare Gemma, Llama, Mistral and Qwen for different use cases
• Estimate RAM and VRAM requirements before running a model
• Understand how quantization affects memory, speed and quality
• Choose between Ollama, llama.cpp, MLX and vLLM
• Connect local models to assistants, RAG systems and applications
• Add sandboxing and guardrails to local agent execution
• Evaluate quality, latency, memory use and regressions
• Decide when to use prompting, RAG or LoRA

By the end, you’ll have a clear decision framework for building local AI systems from hardware and model selection to quantization, inference and application integration.

Meet Your Instructors

Murat Sari

Murat brings hands-on experience from industrial machine vision and local AI systems, where inference must run reliably on-site.

Rainer Hahnekamp

Rainer is a Google Developer Expert, core NgRx maintainer, creator of Sheriff and NgRx Toolkit, and an enterprise software engineer with 15 years of experience.

Together, Murat and Rainer will show you how to make local models work technically and how to integrate them reliably into real software systems.

Who Should Attend?

This workshop is designed for

• Software developers building AI-enabled applications
• AI engineers comparing open models and inference stacks
• Architects evaluating private or sovereign AI
• Platform and DevOps engineers managing model infrastructure
• Security engineers exploring safer local agent execution
• Teams deciding between local models and hosted APIs

No previous experience with local inference engines or fine-tuning is required.

What’s Included

• Three-hour live, hands-on workshop
• Practical model and hardware selection framework
• Guidance across leading local inference tools
• Live Q&A with both instructors
• Full workshop recording
• Certificate of completion