A New Chapter for Kubernetes and ML Inference

As AI adoption accelerates, Kubernetes faces the challenge of going beyond simple container orchestration to efficiently manage a variety of AI accelerators, including GPUs. Existing design approaches have shown limitations in operating ML inference workloads at scale, due to complex hardware topology requirements and fragmented container runtime support.

To address these issues, DRA (Dynamic Resource Allocation) and CDI (Container Device Interface) have emerged as key innovations. In particular, DRA allows the Kubernetes scheduler to delegate resource allocation to specialized plugins, enabling a deep understanding of hardware characteristics and optimal placement of workloads.

FuriosaAI is focusing its engineering capabilities on leveraging these technological advances to seamlessly deploy its high-performance inference chip, RNGD, in Kubernetes environments. The adoption of DRA enables the following benefits:

• Pod scheduling support that accounts for hardware topology
• Resource management that simplifies complex device request and allocation processes
• Native support for partitionable devices