AI infrastructure is growing at an unprecedented pace. Enterprises are racing to build clusters of GPUs, scale up AI workloads, and modernize their data pipelines. Yet one critical layer is often overlooked in these initiatives: the network.
While AI and data leaders focus on compute, storage, and models, the network quietly becomes the bottleneck. Traditional, static networks—built for legacy application traffic—can’t handle the dynamic, latency-sensitive, high-throughput demands of distributed AI workloads. And without visibility, orchestration, and automation across the full stack, enterprise IT leaders are flying blind in one of the most critical infrastructure domains of the decade.
At ONUG, where the community has long championed open, cloud-scale networking, this challenge is both familiar and urgent. It’s time to reframe the conversation: AI networking isn’t a peripheral concern—it’s the missing layer in AI infrastructure. And the solution is not just faster switches. It’s a full-stack, open, and AI-operated networking layer designed for the AI era.
The Blind Spot in AI Buildouts
A New Layer: Full-Stack AI Networking
To solve this, enterprises need to rethink how networks are built and managed—starting with a full-stack approach.
- Networks for AI: The physical and virtual infrastructure optimized to connect GPUs with high-throughput, low-latency, lossless configurations.
- AI for Networks: Intelligent automation powered by AI that simplifies Day 0–2 operations, from deployment to troubleshooting to compliance.
- Open Network Operating Systems (NOS) like SONiC and Cumulus that decouple software from hardware
- Multi-vendor orchestration layers that unify fabrics across OEMs
- Observability and telemetry frameworks—offering deep packet inspection, metadata extraction, and visibility across 4G/5G/AI fabrics
- LLM-based copilots that assist with upgrades, audits, performance tuning, and real-time issue resolution
Why Open Matters More Than Ever
- Tune the network stack based on their specific AI workloads
- Replace and upgrade hardware without rewriting the orchestration playbook
- Integrate seamlessly with observability tools, automation platforms, and security frameworks
From Complexity to Clarity: AI-Powered Operations
Operating AI infrastructure shouldn’t require navigating dozens of tools or relying on tribal knowledge. Networks must evolve to support simplified, AI-powered operations.
- Unifying management across the operations
- Leveraging real-time telemetry for proactive troubleshooting
- Automating repetitive tasks like compliance checks, and performance audits
- Using copilots to generate insights, summaries, and reports that accelerate time to resolution
Build the Right Layer
The time is now to invest in AI networking as a full-stack discipline—not a siloed afterthought. By embracing open, AI-powered, and multi-vendor infrastructure, IT leaders can finally align the network with the speed of innovation in AI.
FAQs
1. Why is AI networking considered the missing layer in enterprise AI infrastructure?
While compute and storage dominate AI infrastructure discussions, the network often becomes the bottleneck. Traditional networks lack the flexibility, observability, and low-latency capabilities needed to support modern, distributed AI workloads at scale.
2. What does a full-stack AI networking architecture include?
A full-stack AI network includes:
- Open NOS like SONiC or Cumulus
- Multi-vendor orchestration layers
- Deep observability with telemetry and metadata inspection
- LLM-powered copilots for upgrades, audits, and troubleshooting
This enables seamless, intelligent, and lossless AI data pipeline operations.
3. How does open networking like SONiC benefit AI infrastructure?
Open networking decouples software from hardware, giving IT teams vendor freedom, better scalability, and faster upgrades—crucial for adapting networks to rapidly evolving AI workloads.
4. What is the role of AI in managing AI networks?
AI is used to power intelligent automation—handling deployment, upgrades, compliance, performance tuning, and real-time troubleshooting, reducing reliance on manual intervention and scripts.
5. How can enterprises future-proof their AI infrastructure with the right networking stack?
By adopting a full-stack, open, and AI-operated network layer, enterprises can reduce costs, boost performance, and scale AI workloads with confidence—ensuring the network is no longer a limiting factor.
