Turbocharge Cloud-Native AI Workloads With DPU-Accelerated Service Proxy for Kubernetes

AI clouds deploy AI applications at a massive scale, typically as containerized workloads accelerated for cloud-native environments. To meet the needs of LLMs, RAGs, and other AI applications, AI cloud infrastructure relies on high-bandwidth, low-latency interconnections between CPU, GPU, memory, and storage.

AI cloud architecture is built using Kubernetes clusters. Kubernetes networking lets containerized applications communicate within the GPU cluster directly. Deployment of AI applications at scale needs high performance, efficient load balancing, application security, auto scaling, and multi-tenancy. An efficient proxy infrastructure service provides ingress/egress access to cluster resources without burdening the CPU and GPU.

Learn how to meet AI cloud demands with a cloud-native, hardware-accelerated proxy. Offloading and isolating application delivery services to a DPU dramatically reduces CPU overhead and boosts infrastructure efficiency while minimizing attack surface.