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Cisco and NVIDIA Partners on AI Infrastructure for Data Centers

Cisco and NVIDIA announced plans to deliver AI infrastructure solutions for the data center that are easy to deploy and manage, enabling the massive computing power that enterprises need to succeed in the AI era.

"AI is fundamentally changing how we work and live, and history has shown that a shift of this magnitude is going to require enterprises to rethink and re-architect their infrastructures," said Chuck Robbins, Chair and CEO, Cisco. "Strengthening our great partnership with NVIDIA is going to arm enterprises with the technology and the expertise they need to build, deploy, manage, and secure AI solutions at scale."

"Companies everywhere are racing to transform their businesses with generative AI," said Jensen Huang, founder and CEO of NVIDIA. "Working closely with Cisco, we're making it easier than ever for enterprises to obtain the infrastructure they need to benefit from AI, the most powerful technology force of our lifetime."

Cisco, with its industry-leading expertise in Ethernet networking and extensive partner ecosystem, together with NVIDIA, the inventor of the GPU that fueled the AI boom, share a vision and commitment to help customers navigate the transitions for AI with highly secure Ethernet-based infrastructure.

Cisco and NVIDIA have offered a broad range of integrated product solutions over the past several years across Webex collaboration devices and data center compute environments to enable hybrid workforces with flexible workspaces, AI-powered meetings and virtual desktop infrastructure. The companies are now deepening their partnership in the data center to enable enterprise customers with scalable and automated AI cluster management, automated troubleshooting, best-in-class customer experiences, and more. Highlights include:

Cisco and NVIDIA Integrated Data Center Solutions available now:

- NVIDIA's newest Tensor Core GPUs are available in Cisco's M7 generation of UCS rack and blade servers, including Cisco UCS X-Series and UCS X-Series Direct, to enable optimal performance across a broad array of AI and data-intensive workloads in the data center and at the edge

- NVIDIA AI Enterprise, which includes software frameworks, pretrained models and development tools for more secure, stable and supported production AI is now available on Cisco's global price list.

- Jointly validated reference architectures through Cisco Validated Designs (CVDs) make it simple to deploy and manage AI clusters at any scale in a wide array of use cases spanning virtualized and containerized environments, with both converged and hyperconverged options. CVDs for FlexPod and FlashStack for Generative AI Inferencing with NVIDIA AI Enterprise will be available this month, with more to follow.

- Supporting Cisco Networking Cloud: Cisco simplified AI infrastructure management and operations through both on-premises and cloud-based management with Cisco Nexus Dashboard and Cisco Intersight.

- Digital Experience Monitoring: With AI workloads and data in the public cloud, on premises and across multiple data centers, ThousandEyes provides Digital Experience Monitoring to provide AI-driven insights and automated remediation of problems that occur anywhere across the cloud to on-premises networks.

- The Cisco Observability Platform uses AI capabilities to contextualize and correlate real-time telemetry across domains, so organizations can better attain the visibility, insights and actions to improve digital experiences.

- Partners Reducing Risks: As organizations plan to successfully adopt AI and automation, they will look to Cisco's global ecosystem of partners to advise, support and guide them.

"As enterprises look to transform their businesses with AI, they must understand the unique demands that AI workloads place on data center infrastructure," said Vijay Bhagavath, Vice President, Cloud & Data Center Networks at IDC Research. "The Cisco and NVIDIA partnership brings together two trusted brands with complementary technologies to enable customers to realize the full potential of AI with a wide range of performance-optimized Ethernet-based infrastructure. "

Availability: 2Q Calendar Year; Solutions sold through Cisco Channel partners.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Cisco and NVIDIA Partners on AI Infrastructure for Data Centers

Cisco and NVIDIA announced plans to deliver AI infrastructure solutions for the data center that are easy to deploy and manage, enabling the massive computing power that enterprises need to succeed in the AI era.

"AI is fundamentally changing how we work and live, and history has shown that a shift of this magnitude is going to require enterprises to rethink and re-architect their infrastructures," said Chuck Robbins, Chair and CEO, Cisco. "Strengthening our great partnership with NVIDIA is going to arm enterprises with the technology and the expertise they need to build, deploy, manage, and secure AI solutions at scale."

"Companies everywhere are racing to transform their businesses with generative AI," said Jensen Huang, founder and CEO of NVIDIA. "Working closely with Cisco, we're making it easier than ever for enterprises to obtain the infrastructure they need to benefit from AI, the most powerful technology force of our lifetime."

Cisco, with its industry-leading expertise in Ethernet networking and extensive partner ecosystem, together with NVIDIA, the inventor of the GPU that fueled the AI boom, share a vision and commitment to help customers navigate the transitions for AI with highly secure Ethernet-based infrastructure.

Cisco and NVIDIA have offered a broad range of integrated product solutions over the past several years across Webex collaboration devices and data center compute environments to enable hybrid workforces with flexible workspaces, AI-powered meetings and virtual desktop infrastructure. The companies are now deepening their partnership in the data center to enable enterprise customers with scalable and automated AI cluster management, automated troubleshooting, best-in-class customer experiences, and more. Highlights include:

Cisco and NVIDIA Integrated Data Center Solutions available now:

- NVIDIA's newest Tensor Core GPUs are available in Cisco's M7 generation of UCS rack and blade servers, including Cisco UCS X-Series and UCS X-Series Direct, to enable optimal performance across a broad array of AI and data-intensive workloads in the data center and at the edge

- NVIDIA AI Enterprise, which includes software frameworks, pretrained models and development tools for more secure, stable and supported production AI is now available on Cisco's global price list.

- Jointly validated reference architectures through Cisco Validated Designs (CVDs) make it simple to deploy and manage AI clusters at any scale in a wide array of use cases spanning virtualized and containerized environments, with both converged and hyperconverged options. CVDs for FlexPod and FlashStack for Generative AI Inferencing with NVIDIA AI Enterprise will be available this month, with more to follow.

- Supporting Cisco Networking Cloud: Cisco simplified AI infrastructure management and operations through both on-premises and cloud-based management with Cisco Nexus Dashboard and Cisco Intersight.

- Digital Experience Monitoring: With AI workloads and data in the public cloud, on premises and across multiple data centers, ThousandEyes provides Digital Experience Monitoring to provide AI-driven insights and automated remediation of problems that occur anywhere across the cloud to on-premises networks.

- The Cisco Observability Platform uses AI capabilities to contextualize and correlate real-time telemetry across domains, so organizations can better attain the visibility, insights and actions to improve digital experiences.

- Partners Reducing Risks: As organizations plan to successfully adopt AI and automation, they will look to Cisco's global ecosystem of partners to advise, support and guide them.

"As enterprises look to transform their businesses with AI, they must understand the unique demands that AI workloads place on data center infrastructure," said Vijay Bhagavath, Vice President, Cloud & Data Center Networks at IDC Research. "The Cisco and NVIDIA partnership brings together two trusted brands with complementary technologies to enable customers to realize the full potential of AI with a wide range of performance-optimized Ethernet-based infrastructure. "

Availability: 2Q Calendar Year; Solutions sold through Cisco Channel partners.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...