Leveraging AI locally to build modern applications quickly, reliably and cost effectively
Software developers typically use tools such as Claude Code or Codex with the vendors’ paid models. As usage grows, however, the associated costs can become significant.
Both tools can also connect to models running locally and avoid subscription fees or per-token charges. Agentic software development is demanding, though, so choosing the right model, hardware, and setup is important.
The workshop covers what developers can realistically expect in terms of quality, what hardware they need, and how to configure Claude Code or Codex to work with local models.
By the end of the workshop, you’ll be able to:
- By the end of the workshop, you'll be able to:
- Understand which local LLMs are suited for software development
- Have an overview of the costs for the infrastructure
- Configure your coding agents, like Claude Code/Codex to connect to your local LLM
You should walk away with:
- Real-world examples of local AI:
- developing
- performance enhancement
- configuration
- A stronger understanding of how to connect development and operations through AI-native workflows
- Live Q&A with the speaker to explore implementation ideas and real-world concerns
You’ll also receive:
✔ full workshop recording
✔ certificate of completion
This workshop is ideal for:
This workshop is ideal for:
- Senior software engineers
- Enterprise executives and decision-makers: Concerned with data governance, operational independence, and long-term cost predictability.
Prerequisites
- Prior knowledge: You should have experience in AI-assisted development
- Students and AI enthusiasts: Interested in exploring the latest local AI capabilities and hardware requirements for their own setups.
Good to know
Highlights
- 1 hour
- Online
Location
Online event
Agenda
Application Use Case and Model Comparison
Present the application use case and show how Claude Opus implemented it. Compare the result with the output and code diffs produced by selected local LLMs, highlighting differences in quality, correctness, and developer effort.
Selecting the Right Local LLM
Introduce the local models chosen for this webinar and explain the selection criteria, including coding performance, context window, hardware requirements, licensing, and practical trade-offs
Setting Up the Inference Machine
Configure the inference machine, serve a local model, and connect Claude Code to it. Walk through the essential settings and verify the setup with a practical coding task.