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Infrastructure to Power AI

Deliver sustainable AI-native infrastructure to the edge, cloud & data center.

A faster, more flexible, more secure infrastructure and network are required to meet the increasing demands of AI training and inference workloads. Cisco helps to scale infrastructure with AI-native, high-density switches, improved network management, high-performance optics, and Cisco Validated Designs (CVDs). Cisco is reinventing data center operations for our customers by simplifying configuration, monitoring and maintenance of all fabrics, compute, networking and storage.

Cisco AI PODs

Advanced, purpose-built infrastructure solutions designed to empower organizations with the capabilities of artificial intelligence. These AI-ready PODs offer scalable and efficient performance, catering to a range of needs from cost-effective development to complex enterprise applications. With pre-configured, ready-to-deploy setups, centralized management, and seamless scalability, Cisco AI PODs simplify the deployment of AI technologies, enabling businesses to drive innovation and maintain a competitive edge in an AI-driven world. Whether you're beginning your AI journey or expanding high-performance workloads, Cisco AI PODs provide the tools and support necessary to unlock AI potential at every stage.

• Validated Designs: Cisco AI PODs are built on Cisco Validated Designs, which are rigorously tested guidelines. These designs help reduce deployment risk by providing proven methods and best practices, optimize performance through efficient configurations, and ensure scalability to accommodate growing AI workloads.
   
• Comprehensive Infrastructure: Cisco AI PODs offer a complete suite of solutions designed for various AI and ML use cases. This comprehensive infrastructure simplifies and automates the deployment of AI systems, making it easier for businesses to implement and manage AI technologies.
   
• Scalability and Flexibility: The AI PODs provide independent scalability at each layer of the infrastructure. This means that resources can be scaled up or down as needed, making them suitable for both data center and edge AI deployments, where different levels of computing power and storage may be required.
   
• Centralized Management and Automation: These solutions include centralized management and automation features, which help accelerate the deployment process and simplify ongoing operations. This centralized approach allows for easier monitoring, control, and optimization of AI infrastructure.
   
• Tailored Configurations: Cisco offers various configurations of AI PODs with NVIDIA accelerated computing to meet specific AI use cases. Whether a business needs edge inferencing capabilities or large-scale data center solutions, Cisco provides tailored setups that align with the unique requirements of different AI applications.
   
• Integration with Leading Technologies: Cisco AI PODs integrate with leading software technologies from including NVIDIA AI Enterprise and Red Hat OpenShift. This integration supports high-performance AI tasks and streamlines machine learning operations, leveraging the strengths of these leading technology providers to enhance the overall capabilities of the AI infrastructure.

Cisco Nexus Hyperfabric AI

Cisco Nexus Hyperfabric AI is a premium solution designed to cater specifically to AI workloads, offering a comprehensive AI infrastructure that integrates the latest NVIDIA accelerated computing technology with Cisco's advanced Ethernet networking and management solutions. This easily deployable, scalable, and manageable AI/ML solution allows customers to build private cloud AI infrastructures with reduced operational overhead. With its cloud-managed, converged Ethernet network and NVDIA AI Enterprise software, Cisco Nexus Hyperfabric AI simplifies the IT lifecycle, enabling IT generalists, data-science teams, and DevOps teams to efficiently design, deploy, and operate AI clusters, making it an ideal choice for organizations looking to innovate and scale their AI capabilities.

• AI Workload Optimization: Cisco Nexus Hyperfabric AI is tailored for AI workloads, integrating the latest accelerated computing infrastructure and AI software from NVIDIA. This ensures that AI applications can run efficiently and effectively, leveraging cutting-edge hardware to optimize performance.
   
• Converged Ethernet Solution: The solution provides a unified Ethernet network that simplifies the deployment, scalability, and management of AI/ML solutions. This convergence allows for seamless integration and operation within existing network infrastructures.
   
• Reduced Operational Overhead: Designed to minimize the complexity of operations, the solution enables IT generalists, data-science teams, and DevOps teams to manage AI infrastructure with ease, reducing the need for specialized skills and resources.
   
• Scalability:  Customers can deploy AI clusters with confidence, utilizing existing skills and processes to support and scale their operations. This flexibility ensures that as demands grow, the infrastructure can expand accordingly.
   
• Simplified IT Lifecycle: The solution streamlines the entire IT lifecycle, from design to deployment and operation, for both AI and non-AI data center fabrics. This simplification allows for more efficient management and quicker adaptation to changing needs.
   
• Partnerships:  Cisco collaborates with NVIDIA and VAST Data to facilitate rapid and reliable AI deployment. These partnerships bring together expertise in networking, computing, and data storage to deliver a comprehensive AI solution.
   
• Cloud Management: Managed by a cloud controller, the solution allows for the global management of data-center network fabrics with minimal expertise. This cloud-based approach provides centralized control and oversight, enhancing operational efficiency.

Security for AI, AI for Security

Cisco Security Cloud is an AI-powered, cloud-native, cloud-delivered, integrated platform that delivers effective, scalable protection to organizations of any shape and size.

• Assistant Experiences: Give security analysts the ability to operate at machine-scale- assisting with complex tasks, saving time, and eliminating errors and misconfigurations
   
• Augment Human Insight: Augment human intelligence with AI-powered detections and intelligence, providing insight into what's going on in an environment and identifying patterns that humans may miss
   
• Automate Complex Actions: Leveraging AI to reduce the time spent on tasks by automating repetitive actions and workflows, so more productive tasks can be prioritized

Cisco Hypershield

A groundbreaking AI-native security architecture designed to defend modern, AI-scale data centers. It uniquely combines security and networking by embedding security into every software component of applications running on networks, servers, and cloud deployments. With AI-powered capabilities, Hypershield automates security policy lifecycles and infrastructure upgrades, providing deep visibility and enforcement down to the kernel level. This innovative solution allows for unprecedented end-to-end security, enabling organizations to set their preferred level of autonomy while ensuring continuous protection and rapid response to threats.
 

• Automated Segmentation Workflows: This involves using AI-driven segmentation to manage rules across various applications and workloads. It reduces the manual effort required by teams by automating the process of defining and enforcing segmentation policies, ensuring that security measures are consistently applied without the need for constant human intervention.
   
• Flexible Segmentation Rules: These are policies that are informed by the application lifecycle and provide deep process visibility. Unlike rigid rules, flexible segmentation rules adapt to changes in application behavior and lifecycle, allowing for more dynamic and responsive security measures that align with the current state of the application.
   
• Single, Global Policy: This refers to the implementation of a consistent policy across the entire network and all workloads. It involves intelligent placement of policies to ensure that security measures are uniformly applied, reducing the complexity and potential for errors that can arise from managing multiple disparate policies.
   
• Surgical Exploit Protection: This is a process-level protection mechanism that blocks exploits while allowing applications to continue running. It involves precise security controls that target specific vulnerabilities without disrupting the overall functionality of the application, ensuring that security measures do not interfere with business operations.
   
• End-to-End Vulnerability Protection: This provides a comprehensive view and prioritization of vulnerabilities within the system. AI-recommended remediations help prioritize which vulnerabilities to address first, based on their severity and potential impact, ensuring that the most critical issues are resolved promptly.
   
• Self-Qualifying Updates: This utilizes dual dataplane technology to test updates without impacting production environments. By replicating live traffic in a shadow data plane, updates can be thoroughly vetted before being applied to the primary data plane, minimizing the risk of disruptions.
   
• AI-Native Security: This security approach is designed with AI at its core, enabling autonomous management and intelligent recommendations. It leverages AI to continuously learn and adapt to new threats, providing proactive and informed security measures.
   
• Kernel-Level Enforcement: This involves using eBPF technology to gain deep visibility and enforcement at the workload level. It allows for fine-grained security controls and monitoring of workload behavior without modifying the kernel, ensuring system stability while enhancing security.
   
• Unified Cloud Management: This is a centralized approach to policy management that dynamically adapts to workload movements. It ensures that security policies are consistently applied across different environments, whether on-premises or in the cloud, and can adjust to changes in workload locations or configurations.
   
• Virtual Machine and Container-Based Enforcement: This refers to network enforcement points that are positioned close to workloads, whether they are virtual machines or containers. It provides effective asset protection by ensuring that security measures are applied as close to the source of potential threats as possible, enhancing the overall security posture.

AI for Productivity

AI enhancements across the Webex platform increase productivity, enhance employee and agent experiences, and reduce burnout. Webex leverages Large Language Models (LLM) and Real-time Media Models (RMM) to improve communication, collaboration, and operational efficiency delivering purpose-built AI for employee and customer experiences by tapping into audio, video and text intelligence to elevate human interactions.

• Generative AI for Operational efficiency – meeting summaries, chat summaries & message translation streamline follow up, improve response times and ensure all team members can participate fully regardless of native language
   
• Audio and Video Intelligence – Noise reduction, face detection and identification and AI audio codecs provide an optimal experience for the demands of Hybrid Work
   
• Improved Customer Interactions – Maintain agent productivity and morale, assist in provide prompt and accurate responses, and capture customer sentiments for proactive service improvements

WWT experts and the AI Proving Ground in our Advanced Technology Center (ATC) support the latest in Cisco AI Solutions.