Networking for AI: the unsung hero of the intelligent enterprise

AI is everywhere right now. It’s powering fraud detection in banks, driving breakthroughs in research labs and helping organisations of all sizes make smarter decisions, faster. However, in all that noise and excitement, there’s one thing that often gets overlooked: the network that makes it all possible.

Let’s be honest, networking isn’t always the headline act. Yet as AI moves from the future into the here and now, the pressure on our networks is ramping up fast. If you’re leading IT in a large enterprise, financial services firm, research institute, hyper-scaler or a next-generation GPUaaS provider (Neocloud), you’re probably already feeling it.

Why networking for AI is different

Picture a traditional data centre as a library. You ask for a book, the librarian fetches it, and off you go. It’s straightforward, with simple, predictable north-south traffic. Requests in, results out.

AI flips that model on its head. Now imagine you’re not just after one book, you need snippets from thousands of books, all at once, to create an entirely new bespoke book. Meanwhile, a team of librarians is frantically cross-referencing, comparing notes and assembling pages in real time. Suddenly, you’ve got a storm of east-west traffic flying back and forth, where speed and coordination are critical. This requires high throughput, low latency and expert congestion management.

In a data centre designed to support AI workloads, it’s not just one network doing all the heavy lifting, there are typically three networks working in harmony:

•  The east-west network is the powerhouse, moving huge amounts of data rapidly between GPUs to keep AI workloads running smoothly.
•  The north-south network connects everything to the outside world and high-speed storage.
•  The out-of-band management network, quietly handling device management and troubleshooting. Think of it as the backstage crew, keeping everything running smoothly behind the scenes.

There’s a lot more choreography than the traditional data centre dance!

The new rules of the game

Supporting AI isn’t simply a case of adding more bandwidth or buying faster switches. It’s about rethinking the whole approach from the ground up. AI workloads generate dense east-west traffic, with servers and GPUs talking to one another constantly, collaborating in real time to process and analyse vast amounts of information. This creates a network environment where ultra-low latency and massive throughput are no longer ‘nice to have’ – they are essential. Every millisecond counts and even the smallest delay can ripple through the system, slowing down model training or real-time inference and ultimately impacting business outcomes. With the high cost of building and running these environments, time really does mean money. 

Then there’s the question of how you build it. The protocols you choose play a huge role in how effectively your network can support AI. Technologies like RoCEv2, InfiniBand and the emerging Ultra Ethernet are all vying for the spotlight, each with their own strengths and trade-offs. Some are easier to deploy because they use standard Ethernet, making them more accessible for organisations looking to evolve their existing infrastructure, utilise existing skillsets and leverage familiar networking platforms. Others, like InfiniBand, offer blistering speed and ultra-low latency but require specialised hardware and expertise.

The right choice depends on your goals, budget, current environment and how quickly you need to scale. It’s a balancing act between performance, complexity and futureproofing your investment – there’s no one-size-fits-all approach.

Collaboration, not fragmentation

One of the most exciting trends we’re seeing is how vendors are collaborating to make life easier for customers. A standout example is the Cisco and Nvidia partnership, working together to combine Cisco’s robust switching with Nvidia’s AI-optimised network cards. This allows organisations to build flexible, unified architectures without getting locked into a single vendor, meaning faster deployments, simpler management and confidence in your network that it can keep up as your AI ambitions grow.

Security still comes first

With all this speed and complexity, security can’t be an afterthought. As AI networks get bigger, the risks grow too. The challenge is to protect sensitive data and ensure compliance without slowing down performance. That’s why solutions like Cisco Hypershield and Isovalent are being integrated right into the network fabric, providing robust protection even as traffic patterns become more dynamic and unpredictable. As AI environments grow, having security that’s both seamless and scalable is key to keeping your operations safe and resilient.

Bringing it all together

AI is transforming industries and driving innovation, but it needs the right network to truly deliver results. A well designed AI-ready network doesn’t just keep things running, it unlocks performance, agility, real business value and genuine impact.

At Softcat, we know that every organisation’s AI journey is unique. Whether you’re a financial services giant looking to accelerate analytics, a research institute pushing the boundaries of science, or a neocloud provider enabling others to build the next big thing, we’re here to help. Our team works alongside you to define, design and deploy networking solutions that are ready for the demands of AI today and tomorrow.

To explore how your organisation can enter the world of networking for AI or take your existing network investments to the next level, contact your Softcat account team or use our sales form, to start the conversation.