Sending massive volumes of proprietary enterprise source code to external cloud AI APIs introduces severe challenges around network latency, API costs, and data data privacy. The alternative? Moving your execution boundaries to the edge by deploying Small Language Models (SLMs) locally on engineering hardware.
This deep technical session deep dives into the mechanics of running and orchestrating local developer agents. Leveraging the high-performance Go-based architecture of Ollama alongside lightweight models like Phi, we look at how to maximize local hardware compute for agentic workflows. We cover the engineering realities of running parallel local workloads, context window optimization strategies, and the design patterns necessary to handle fallback routines when local models hit reasoning limitations. If you want to build fast, private, and zero-cost AI automations directly on developer machines, this session is for you.

What you will learn:

- Edge Architecture: Setting up and scaling local SLM endpoints utilizing Ollama's high-performance runtime engine.
- Resource Engineering: Managing memory, inference latency, and concurrency constraints when running multi-agent tasks locally.
- Hybrid Failover Design: Implementing secure, conditional routing patterns to pass complex reasoning tasks to cloud environments only when required.