With the development of the AI, more and more applications, e.g. LLM, asks for a better solution of network and storage to work together with GPU. xPU, including both DPU and IPU, is one of well-known solution which provides offload, hardware acceleration and so on. But an AI server usually includes serveral devices to maximize the performance, e.g. 8 DPU + 10 xPU, which introduces the challenge to manage all of those devices. Thanks to kubernetes, the GPU ares managed will by device plugin and operator; but xPU management is more complex which Including provisioning, storage and network.

This session will demonstrate how to manage those xPU in a large scale by some use cases and share the pain-point and solution accordingly; this session will also include part of the roadmap of the system, and general tips for xPU.