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In the early days of AI infrastructure, system design was driven by compute horsepower. The more GPUs you could string together, the better. But as models balloon into the hundreds of billions of parameters and data sets scale into the petabytes, it’s no longer just about how much compute you have — it’s about how well it all works together. 

And increasingly, that comes down to the network. 

If you’re designing for scale — real, production-grade scale — the question isn’t just “How many GPUs?” but “How will they communicate?” That’s why Cornelis Networks took a bold step forward with the new CN5000 Omni-Path^® network (sampling now) and the industry’s only modular, high-radix Director-Class Switch, purpose-built for AI and HPC. 

In this post, we’ll unpack how a fabric-centric design, anchored by our modular topologies, enables faster performance, simpler operations, and more cost-effective scaling.  

The Network Is Now the Accelerator 

In today’s AI clusters, the network isn’t just a transport layer — it’s a first-class component of system performance. As workloads become more distributed, training and inference pipelines depend on fast, reliable communication between nodes. This is especially true for: 

• Large language models spanning thousands of GPUs 

• Data parallel workloads with collective operations 

• Edge-to-core pipelines with high-throughput requirements 

But while traditional spine-leaf Ethernet fabrics were designed for general-purpose traffic, they struggle to keep up with the tight synchronization, high throughput and low-latency demands of AI workloads at scale. 

That’s where the CN5000 generation of Omni-Path changes the game. 

Network-Centric Design: Optimized for Performance and Simplicity 

Our approach starts with a question: What if the network was designed specifically for AI from day one?
Answer: you would design for congestion-free performance, scalability, and flexibility from the beginning resulting in a modular architecture anchored by the industry’s only modular, high-radix Director-Class Switch — purpose-built for scalable AI and HPC workloads. 

Why It Matters: 

• Lower Operational Complexity
  Unlike traditional networks where spine and leaf switches require extensive cabling, our Director-Class Switch eliminates spine-leaf cabling altogether. That means:

• • No cable spaghetti between leaf and spine 

• • Faster, cleaner installs 

• • Fewer points of failure 

• • Easier diagnostics and cable tracing 

• Lower Power Consumption
  Each optical link between spine and leaf adds power draw. By removing those links, the CN5000 Director-Class Switch cuts power overhead significantly, freeing up energy headroom for compute — not infrastructure.

• Reduced Rack Space (and Smarter Use of It)
  The CN5000 Director-Class Switch delivers incredible port density, with 576 non-blocking 400 Gbps ports delivered in a modular form factor requiring only 17RU of space.  This frees up space for compute or storage nodes. More room. Freed-up power budget. Better density. And with the Director-Class Switch placed at row end or mid-row, you also streamline cable routing.

• High Availability with Full Redundancy
  The CN5000 solutions provide a fault-tolerant architecture with full system-level redundancy— delivering high reliability without layering on complexity.

• Lower Upfront Capital Cost and Better TCO
  By reducing the number of switches, cables, and the energy needed to support it all, the CN5000 architecture lowers up-front CapEx — and continues to deliver TCO savings through:

• • Less power draw 

• • Simpler operations 

• • Smaller network management footprint

In-Place, Modular Scaling 

Need to scale later? Just add more leaf modules and wire up more nodes. No need to rearchitect your spine or bring down the network. With pre-populated spine modules, your fabric is ready to grow when you are. If your scaling needs are even higher, Cornelis also offers switching solutions that support up to 250,000 nodes in a multitude of advanced low-diameter topologies. 

A Smarter Way to Build AI Infrastructure

At its core, CN5000 Omni-Path fabric isn’t just about speed — it’s about smarter infrastructure design. It supports topologies like Dragonfly+, mesh, or custom configurations to align with your workload’s communication needs. And it’s inherently more scalable architecture, it scales easily while keeping operational friction low. 

The result? 

• Linear performance scaling 

• Predictable training throughput 

• Higher GPU utilization 

• Easier deployment and day-2 operations 

In short, a fabric that works with you — not against you. 

Final Word: Infrastructure Shouldn’t Be the Bottleneck

 AI models will keep growing. Training jobs will get more complex. But your infrastructure shouldn’t have to grow in complexity along with them. 

With the CN5000 Omni-Path solutions, Cornelis gives AI and HPC architects a way to build high-performance systems that scale with elegance — not just brute force.
From lower power and rack footprint to better economics and availability, this is what fabric-centric design should look like. 

Ready to build AI infrastructure that scales smarter?

Let’s talk about how CN5000 Omni-Path networking can power your next-generation cluster.  Sampling now.  Contact sales@cornelisnetworks.com for more information.