AI Inference in the Real World: From Kubernetes to the Far Edge

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*AI workloads don’t all belong on the same model, hardware, or infrastructure.* For our **13th AI Native Netherlands meetup**, Christian Melendez from [AWS](https://aws.amazon.com/) will look at when smaller models and CPUs make more sense than defaulting to large GPU-backed LLMs. William Rizzo from Mirantis will take that question to the far edge: running LLM inference on immutable, disconnected clusters where connectivity is unreliable and remote access can’t be assumed. **Together, the talks explore one practical question:** *What should run where, on what hardware, and how do you keep it reliable in production?* A huge thank you to our friends at **AWS** for hosting us at their Amsterdam office. Food and drinks will be provided! **We'll cover:** * When AI workloads actually need GPUs — and when CPUs and smaller models are the better fit. * How to combine Small Language Models and larger LLMs without sending every task to the most expensive model. * What changes when inference moves from the datacenter to factories, vehicles, retail sites, and other edge environments. * How immutable, image-based infrastructure can support updates, rollbacks, and reliability across disconnected fleets. * The real-world trade-offs, failure modes, and production lessons behind both approaches. **Speaker 1: [Christian Melendez (AWS)](https://www.linkedin.com/in/christianmldz/)**[ ](https://www.linkedin.com/in/christianmldz/) Christian Melendez is a Principal Specialist Solutions Architect at AWS. He helps the region's largest enterprises build efficient, resilient AI and cloud-native workloads on Kubernetes, with a focus on compute efficiency, cost optimisation, and autoscaling at scale. Author of *Kubernetes Autoscaling* and creator of *Karpenter Blueprints*, a best-practices repository that grew Karpenter adoption 16.5x across EMEA, he also built *Slemify*, an open-source framework for fine-tuning and serving Small Language Models on Kubernetes. A regular speaker at AWS re

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