Cisco is taking a collaborative approach to helping enterprise customers build AI infrastructures.\n\nAt its recent partner summit, Cisco talked up a variety of new programs and partnerships aimed at helping enterprises get their core infrastructure ready for AI workloads and applications.\n\n\u201cWhile AI is driving a lot of changes in technology, we believe that it should not require a wholesale rethink of customer data center operations,\u201d said Todd Brannon, senior director, cloud infrastructure marketing, with Cisco\u2019s cloud infrastructure and software group.\n\nAs AI projects move from science projects in an organization\u2019s backroom to mission-critical applications, enterprise infrastructure and operations teams are being challenged because they are\u00a0dealing with new workloads\u00a0running on familiar infrastructure but with new requirements, Brannon said.\n\n\u201cThe idea is that we want to help our customers\u00a0deploy and manage AI workloads efficiently, find that right mix of acceleration, and not over provision or leave stranded resources or create new islands of operations,\u201d added Sean McGee, cloud & data center technology strategist with Cisco.\u00a0\n\nOne of the ways Cisco intends to help customers is by offering a suite of validated designs that can easily be deployed as enterprise AI needs evolve. \n\nThe company recently announced four new Cisco Validated Designs for AI blueprints from Red Hat, Nvidia, OpenAI, and Cloudera to focus on virtualized and containerized environments as well as converged and hyperconverged infrastructure options. Cisco already had validated AI models on its menu from AMD, Intel, Nutanix, Flashstack and Flexpod.\n\nThe validated designs allow customers to use these models and fine tune what they want to do for their business, McGee said.\n\nCisco is building Ansible-based automation playbooks on top of these models that customers can use with Cisco\u2019s Intersight cloud-based management and orchestration system to automatically inject their own data into the models and build out repositories that can be used in their infrastructure, including at the edge of the network and in the data center, McGee said.\n\nCisco\u2019s Intersight package manages a variety of systems from Kubernetes containers to applications, servers, and hyperconverged environments from a single location.\n\n\u201cUtilizing Intersight and our systems stack, customers can deploy and manage AI-validated workloads,\u201d Brannon said. \u201cThe message is that we don't want our customers and partners having to completely rethink the operation side, even though they're having to rethink some things on the GPU provisioning side for AI, for example,\u201d Brannon said.\n\nIn addition, as Cisco gets feedback from its customers on AI-specific features or additional validated designs, it will augment Intersight with new features, Brannon said.\n\nAlso, over time these models will evolve as more data is used to tune them, and customers can easily adjust them to fit the needs of their enterprise infrastructure, McGee said. \u201cOur partners, too, can utilize these models to significantly expand their services. [They can] really give them a head start and relieve a lot of the engineering expense and time that they need to put these services together for customers.\u201d\n\nCisco recently unveiled Data Center Networking Blueprint for AI\/ML Applications that defines how organizations can use existing data center Ethernet networks to support AI workloads now.\n\nA core component of the data center AI blueprint is Cisco\u2019s Nexus 9000 data center switches, which support up to 25.6Tbps of bandwidth per ASIC and \u201chave the hardware and software capabilities available today to provide the right latency, congestion management mechanisms, and telemetry to meet the requirements of AI\/ML applications,\u201d Cisco stated. \u201cCoupled with tools such as Cisco Nexus Dashboard Insights for visibility and Nexus Dashboard Fabric Controller for automation, Cisco Nexus 9000 switches become ideal platforms to build a high-performance AI\/ML network fabric.\u201d\n\nCisco has also published scripts so customers can automate specific settings across the network to set up this network fabric and simplify configurations, Cisco stated.