Running open models locally changes how engineers think about agent systems. Once models move from hosted APIs into self-managed infrastructure, problems that were previously hidden behind providers become operational realities: latency, orchestration, throughput, observability, GPU memory, tool-call reliability, evaluation variance, and cost control.

This talk shares lessons learned building and operating self-hosted agent systems with llama.cpp, vLLM, quantized models, local coding agents, and production-like homelab infrastructure. Using examples from Pedro CLI, multi-model serving, evaluation workflows, and distributed inference experiments, we will look at what changes when the model is no longer an API call but part of the platform.

Rather than focusing on benchmarks or hype, this session focuses on the operational work required to make open models useful in agent workflows: routing, retries, context management, observability, quantization tradeoffs, and failure recovery.

Attendees will leave with a practical model for deciding when self-hosting makes sense, what infrastructure problems to expect, and how to design open agent systems that are reliable enough to operate.