Introduction
The AI expansion phase is in full swing, with companies pouring hundreds of billions into GPU clusters. While the spotlight stays on compute, many business models are forming in the network layer from fully integrated and pre-validated designs, to best-of-breed DIY, to everything in between. As AI models explode in size and organizations race to move from centralized training to distributed inferencing at the edge, we expect some customers to use a disaggregated approach for more customization and cost savings.
Why Vertical Integration Is Breaking Under AI Demands
Proprietary systems from legacy vendors force organizations into multi-year lock-in cycles. When a new silicon generation arrives (every 12-18 months now), you can’t simply swap ASICs and are stuck waiting for the vendor’s full stack refresh, which often lags 2-3 years behind the GPU roadmap. The result is an over-provisioned or under-provisioned network, depending on which GPU cycles the customer picks. Over-provisioned networks running at 50-60% utilization or additional upgrades can increase costs. This burden can grow as an organization moves through lifecycle management that spans more than one GPU supplier.
Looking at the Arrcus ASC-AI Solution: Real Numbers from Real Deployments
Network disaggregation, separating the network operating system from the underlying hardware, eliminates these constraints. Companies deploying disaggregated solutions like Arrcus ArcOS can achieve 40%+ total cost of ownership (TCO) savings in production AI environments today.
Here’s a typical breakdown we’ve seen in large-scale AI cluster deployments:
| Cost Category | Traditional Integrated | Disaggregated (Arrcus ACE-AI) | Savings |
| Hardware Acquisition (CapEx) | $100M baseline | $55-65M | 35-45% |
| Software Licensing/Support | $25-35M over 5 years | $8-12M | 60-70% |
| Power & Cooling | $18M annually | $14-16M annually | 10-20% |
| Operations & Maintenance | $15-20M annually | $6-9M annually | 55-65% |
| Total 5-Year TCO | $350-400M | $200-240M | 40-45% |
Customer Examples
Actapio, the Japanese AI supercomputer operator, selected Arrcus to build out its next-generation LLM training clusters because of these economics. Similar outcomes have been realized by other cloud builders who can now mix Broadcom, Cisco, Marvell, and NVIDIA silicon freely while running a single, hardware-agnostic network OS.
Time-to-deployment tells an equally compelling story. Disaggregated deployments with Arrcus can achieve full production readiness in 6-8 weeks, which can be a 70-80% reduction. That speed directly translates into revenue: every month earlier you bring inference capacity online is millions in additional generative AI services.
Edge Inferencing
The economic pressure intensifies when you move beyond the core data center. Edge inferencing requires smaller locations with strict power envelopes and minimal operations staff. Vertically integrated solutions don’t scale economically here once you go beyond one metro location or a country’s geographic boundary.
Disaggregated networks solve this by running the same validated ArcOS image from 800 Gbps core fabrics down to 100 Gbps edge leafs. Organizations report being able to deploy inference points at 60-70% lower operational cost than prior alternatives. This can be the difference in making geographically distributed AI profitable.
Conclusion
In the AI era, network economics will be a key differentiator in overall solution costs and profitability. Customers who cling to legacy approaches will find themselves competitively disadvantaged by their own infrastructure costs. Those embracing disaggregation can capture 40%+ TCO advantages while moving at the speed AI demands.