Cisco is making big moves in the tech world by integrating artificial intelligence into its operations and products. The company uses AI internally to improve how it delivers services and helps its customers do the same. With a focus on infrastructure, security, and large-scale networks, Cisco is shaping the future of smart systems for the AI era.

Building a Robust AI Foundation

At the core of Cisco’s AI efforts is a shared AI fabric that combines powerful computing and networking. This setup is built on years of testing and refining, giving Cisco the confidence to offer battle-hardened AI solutions to clients. The infrastructure relies heavily on high-performance GPUs, but it’s not just about raw power. The company emphasizes the seamless integration of compute and network layers, which is crucial for training AI models and handling real-time inference loads efficiently.

This sophisticated infrastructure supports both the training of complex AI models and their deployment in live environments. By optimizing how compute and networking work together, Cisco can deliver faster, more reliable AI-driven services. This approach ensures that AI systems are scalable, secure, and ready for enterprise use.

Advancing Network Automation and AI Workloads

Cisco is a leader in network automation, using AI to streamline configurations and manage identities. These innovations allow for rapid deployment of networks, often generated through natural language commands. This makes setting up and managing large networks much quicker and easier for organizations.

To support the next generation of AI applications, Cisco has released new hardware and orchestration tools. For example, a collaboration with NVIDIA led to the development of new switches and the Nexus Hyperfabric line of AI network controllers. These tools simplify the complex task of deploying high-performance AI clusters, making it more accessible for organizations to scale their AI initiatives.

Additionally, Cisco’s Secure AI Factory framework, developed with partners like NVIDIA and Run:ai, provides a production-ready environment for AI pipelines. It manages distributed orchestration, GPU utilization, Kubernetes microservices, and storage, all integrated under Cisco’s Intersight platform. This ensures that AI workloads are efficient, secure, and ready for enterprise deployment.

Extending AI to the Edge

Cisco also focuses on bringing AI processing closer to where data is generated—at the edge. Its Unified Edge platform combines compute, networking, security, and storage in one package. This setup is ideal for environments where latency is critical, such as manufacturing plants or smart cities.

Rather than creating entirely new solutions for industrial IoT, Cisco extends its data center operational models to edge sites. This means applying the same security policies and management standards at remote locations as in the data center. It allows organizations to maintain high levels of security and control even at distributed sites, making edge AI deployment more straightforward and trustworthy.

By aligning cloud and edge strategies, Cisco aims to deliver consistent, secure, and efficient AI performance across all environments. This approach helps organizations leverage AI not just centrally, but right where the data is produced, enabling faster insights and smarter decision-making.

Inspired by

• https://www.artificialintelligence-news.com/news/how-cisco-builds-smart-systems-for-the-ai-era/

Sources

• cisco.com
• cisco.com
• cisco.com
• ai-expo.net
• techexevent.com