A Hardware-First Approach to Multi-Tenant Segmentation in AI Clouds (www.ori.co)

Ori has announced a groundbreaking hardware-first approach to multi-tenancy in AI cloud environments, addressing the limitations of traditional virtual private cloud (VPC) models. The new architecture ensures secure segmentation and reliable performance for GPU-centric workloads, which is crucial given the high stakes of latency and resource utilization in AI applications. By moving beyond software-based segmentation, Ori integrates hardware-level technologies that provide end-to-end isolation across computing, storage, and networking. This results in a unified framework that delivers the performance of bare-metal systems while maintaining the flexibility of a shared cloud environment. The significance of Ori's approach lies in its ability to eliminate the "noisy neighbor" problem and security risks associated with conventional multi-tenancy solutions. By leveraging NVIDIA's Multi-Instance GPU (MIG) for partitioning and utilizing Single Root I/O Virtualization (SR-IOV) for both GPUs and network interfaces, the architecture ensures each tenant's workload operates without interference. Additionally, the Ori Global Control Plane centrally defines security contexts while enforcing them as close to the hardware as possible, allowing for high-performance, secure AI workloads. This innovation sets a new benchmark for the AI/ML community, facilitating a more efficient and secure deployment of AI infrastructures tailored to the unique demands of compute-heavy applications.