Open & Disaggregated: The Right Architecture for AI Datacenters

AI datacenters are different from traditional enterprise datacenters. In a normal environment, applications may tolerate moderate latency, oversubscription, and occasional congestion. In an AI cluster, the network becomes part of the compute engine. GPUs exchange massive volumes of data during training and inference. When the network slows down, expensive compute capacity waits idle.

That is why the architecture of an AI datacenter must be designed around performance, openness, automation, and operational control.

The traditional model was simple: buy a vertically integrated network stack from one vendor and operate it as a closed system. That approach can work, but it can also limit flexibility, increase dependency, and make lifecycle management expensive. AI infrastructure requires another model: open, disaggregated, and programmable.

Disaggregation separates hardware from software. Instead of tying the datacenter to a single proprietary stack, operators can select switching hardware, network operating systems, automation tools, and telemetry platforms based on technical and economic requirements. SONiC is central to this shift. The SONiC Foundation describes SONiC as an open-source Linux-based network operating system that runs on switches from multiple vendors and ASICs, with network functions such as BGP and RDMA hardened in large cloud datacenters.

For AI datacenters, this matters because the network must scale quickly. A typical design uses a leaf-spine Ethernet fabric, high-speed interfaces, ECMP routing, lossless or near-lossless transport for GPU communication, strong telemetry, and automated provisioning. The objective is to deliver predictable east-west traffic performance between GPU servers.

Open Ethernet fabrics are becoming a credible alternative to more closed high-performance interconnect models. Cisco's SONiC positioning highlights the relevance of open networking for AI and ML clusters, including support for high-speed platforms and AI-class fabrics. The direction is clear: AI networking is moving toward open standards, automation, and vendor-neutral operating models.

An open architecture also improves procurement strategy. In fast-moving markets, GPU availability, switch lead times, optics pricing, and support models change quickly. A disaggregated approach gives infrastructure teams more options. It reduces the risk of being locked into a single supplier at exactly the moment when flexibility matters most.

However, open does not mean simple. SONiC and disaggregated networking require strong engineering discipline. Teams must validate hardware compatibility, software versions, optics, routing design, automation workflows, failure scenarios, monitoring, and support responsibilities. The architecture must be tested before production, not discovered during an outage.

For Morocco and Africa, this is especially important. AI datacenters on the continent must be cost-efficient, scalable, and locally operable. Open networking can support those goals, but only if deployed with proper design governance.

A strong AI datacenter fabric should include:

• A routed leaf-spine underlay using BGP for scale and stability.
• High-speed Ethernet sized for the GPU workload, not only for average traffic.
• RDMA/RoCE readiness where required by the compute platform.
• EVPN/VXLAN or equivalent segmentation when multi-tenant or cloud-like services are needed.
• Telemetry and observability for latency, drops, congestion, optics, and fabric health.
• Infrastructure-as-code automation to avoid manual configuration drift.
• A clear support model across hardware, NOS, optics, cabling, and orchestration.

The strategic point is this: an AI datacenter is not only a building with GPUs. It is a distributed system, and the network is the backplane of that system. Open and disaggregated architectures allow African operators, cloud providers, universities, and large enterprises to build infrastructure that is scalable, transparent, and economically sustainable.

ODDnet's position is practical: use openness where it creates control and value, but combine it with rigorous validation, documentation, and operational readiness. Open networking is not about experimenting in production. It is about building a stronger foundation for the AI era.