The Challenge: Why Standard Isolation Fails on Bare Metal

Delivering true isolation on an AI cluster is significantly harder than in traditional networks. Standard host-based isolation mechanisms simply break down under the massive performance demands of modern GPUs.

• The Virtual Machine and IOMMU Bottleneck: Traditional VMs use the CPU's IOMMU to prevent cross-tenant data leaks. However, AI workloads require high-bandwidth RDMA transfers directly between NICs and GPUs. Enabling the IOMMU forces this traffic through the CPU, crippling throughput, but disabling it eliminates secure memory isolation entirely.
• The Limitations of Containers: Containers provide weaker security due to known escape vulnerabilities. Crucially, they fail entirely when different AI teams require custom OS kernels, specific GPU drivers, or conflicting Kubernetes configurations.

• The Burden of Manual Configuration: This leaves the physical network as the only viable security boundary. While switches can isolate hosts using BGP/EVPN, VXLAN, and ACLs, manually configuring these protocols across dozens of devices is slow, incredibly error-prone, and destroys the self-service cloud experience.
• The Danger of "AI Ops" for Networking: Relying on LLMs to generate complex switch configurations is a dangerous gamble. Adding one tenant alters hundreds of lines of code across devices, producing diffs too complex for meaningful human review. If an LLM hallucinates a single route-map entry, tenant data leaks globally.