OSS + ELC EU 2026

As AI workloads move into production in cloud native environments, the open source community must define the future of AI inference infrastructure. This panel convenes experts from leading projects to assess the ecosystem and chart a path forward.

Key themes include:

- The evolution of cloud native inference (from serverless to large‑scale distributed serving)
- Performance and efficiency advances such as KV cache management, quantization and hardware acceleration
- The need for standardization across APIs, model formats and interoperability.

They’ll also examine how projects like vLLM, llm‑d, and KServe can collaborate to reduce fragmentation.

Panelists will address real‑world challenges in Kubernetes deployment, including multi‑tenancy, GPU sharing, autoscaling and observability. There will also explore emerging trends such as edge inference, multi‑modal models and the convergence of training and inference systems.