Skip to main content

Juniper Networks Introduces Ops4AI Lab

Juniper Networks® announced a multivendor lab for validating end-to-end automated AI Data Center solutions and automated operations with switching, routing, storage and compute solutions from leading vendors, as well as new Juniper Validated Designs (JVDs) that accelerate the time-to-value in deploying AI clusters.

In addition, Juniper is releasing new key software enhancements that optimize the performance and management of AI workloads over Ethernet. Through these Operations for AI—Ops4AI—initiatives, Juniper is collaborating closely with a broad range of infrastructure ecosystem partners to enable the best AI workload performance via the most flexible and easiest-to-manage data center infrastructures.

As a key element of Juniper’s AI-Native Networking Platform, the existing Networking for AI solution consists of a spine-leaf data center architecture with a foundation of AI-optimized 400G and 800G QFX Series Switches and PTX Series Routers. The solution is secured via high performance firewalls with industry-leading effectiveness, and managed via Juniper Apstra data center assurance software and the Marvis Virtual Network Assistant (VNA). Juniper Apstra and Marvis provide key Ops4AI capabilities, such as intent-based networking, multivendor switch management, application / flow / workload awareness, AIOps proactive actions and a GenAI conversational interface. With Juniper’s full Networking for AI solution, customers and partners can lower AI training Job Completion Times (JCTs), reduce latency during inferencing and increase GPU utilization while decreasing deployment times by up to 85 percent and reducing operations costs by up to 90 percent in some instances.

To simplify AI clusters and maximize network performance even further, Juniper has added new Ops4AI software enhancements that together offer unique value for customers. The enhancements being announced today include:

- Fabric autotuning for AI: Telemetry from routers and switches are used to automatically calculate and configure optimal parameter settings for congestion control in the fabric using closed-loop automation capability in Juniper Apstra to deliver peak AI workload performance.

- Global load-balancing: An end-to-end view of congestion hotspots in the network (i.e. local and downstream switches) is used to load-balance AI traffic in real-time, delivering lower latency, better network utilization and reduced JCTs.

- End-to-end visibility from network to SmartNICs: Provides an end-to-end holistic view of the network, including SmartNICs from Nvidia (BlueField and ConnectX), and others.

Juniper has launched the Ops4AI Lab with participation from Juniper’s partner ecosystem including Broadcom, Intel, Nvidia, WEKA and other industry leaders. The Ops4AI Lab, located at Juniper’s Sunnyvale, CA corporate headquarters, is open for all qualified customers and partners who want to test their own AI workloads using the most advanced GPU compute, storage technologies, Ethernet-based networking fabrics and automated operations. Ops4AI Lab testing using validated Ethernet fabrics delivers comparable performance to InfiniBand-based AI infrastructure.

Juniper Validated Designs are detailed implementation documents that give new customers confidence that the solution and topology they have chosen is well characterized, well tested and repeatable, resulting in faster time to successful deployment. All JVDs are proven integrated solutions, tested in best practice designs based on specific platforms and software versions.

Juniper has released the first pre-validated blueprint specifically for AI data centers, built on Nvidia A100 and H100 compute, storage from Juniper’s ecosystem partners, and Juniper’s portfolio of data center leaf and spine switches. This new Ops4AI JVD complements Juniper’s existing JVDs for automated, secure data centers which include QFX and PTX spines, QFX leaf switching, data center automation, and Juniper’s SRX and vSRX/cSRX solutions for data center security.

The Latest

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

Almost half (48%) of employees admit they resent their jobs but stay anyway, according to research from Ivanti ... This has obvious consequences across the business, but we're overlooking the massive impact of resenteeism and presenteeism on IT. For IT professionals tasked with managing the backbone of modern business operations, these numbers spell big trouble ...

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Juniper Networks Introduces Ops4AI Lab

Juniper Networks® announced a multivendor lab for validating end-to-end automated AI Data Center solutions and automated operations with switching, routing, storage and compute solutions from leading vendors, as well as new Juniper Validated Designs (JVDs) that accelerate the time-to-value in deploying AI clusters.

In addition, Juniper is releasing new key software enhancements that optimize the performance and management of AI workloads over Ethernet. Through these Operations for AI—Ops4AI—initiatives, Juniper is collaborating closely with a broad range of infrastructure ecosystem partners to enable the best AI workload performance via the most flexible and easiest-to-manage data center infrastructures.

As a key element of Juniper’s AI-Native Networking Platform, the existing Networking for AI solution consists of a spine-leaf data center architecture with a foundation of AI-optimized 400G and 800G QFX Series Switches and PTX Series Routers. The solution is secured via high performance firewalls with industry-leading effectiveness, and managed via Juniper Apstra data center assurance software and the Marvis Virtual Network Assistant (VNA). Juniper Apstra and Marvis provide key Ops4AI capabilities, such as intent-based networking, multivendor switch management, application / flow / workload awareness, AIOps proactive actions and a GenAI conversational interface. With Juniper’s full Networking for AI solution, customers and partners can lower AI training Job Completion Times (JCTs), reduce latency during inferencing and increase GPU utilization while decreasing deployment times by up to 85 percent and reducing operations costs by up to 90 percent in some instances.

To simplify AI clusters and maximize network performance even further, Juniper has added new Ops4AI software enhancements that together offer unique value for customers. The enhancements being announced today include:

- Fabric autotuning for AI: Telemetry from routers and switches are used to automatically calculate and configure optimal parameter settings for congestion control in the fabric using closed-loop automation capability in Juniper Apstra to deliver peak AI workload performance.

- Global load-balancing: An end-to-end view of congestion hotspots in the network (i.e. local and downstream switches) is used to load-balance AI traffic in real-time, delivering lower latency, better network utilization and reduced JCTs.

- End-to-end visibility from network to SmartNICs: Provides an end-to-end holistic view of the network, including SmartNICs from Nvidia (BlueField and ConnectX), and others.

Juniper has launched the Ops4AI Lab with participation from Juniper’s partner ecosystem including Broadcom, Intel, Nvidia, WEKA and other industry leaders. The Ops4AI Lab, located at Juniper’s Sunnyvale, CA corporate headquarters, is open for all qualified customers and partners who want to test their own AI workloads using the most advanced GPU compute, storage technologies, Ethernet-based networking fabrics and automated operations. Ops4AI Lab testing using validated Ethernet fabrics delivers comparable performance to InfiniBand-based AI infrastructure.

Juniper Validated Designs are detailed implementation documents that give new customers confidence that the solution and topology they have chosen is well characterized, well tested and repeatable, resulting in faster time to successful deployment. All JVDs are proven integrated solutions, tested in best practice designs based on specific platforms and software versions.

Juniper has released the first pre-validated blueprint specifically for AI data centers, built on Nvidia A100 and H100 compute, storage from Juniper’s ecosystem partners, and Juniper’s portfolio of data center leaf and spine switches. This new Ops4AI JVD complements Juniper’s existing JVDs for automated, secure data centers which include QFX and PTX spines, QFX leaf switching, data center automation, and Juniper’s SRX and vSRX/cSRX solutions for data center security.

The Latest

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

Almost half (48%) of employees admit they resent their jobs but stay anyway, according to research from Ivanti ... This has obvious consequences across the business, but we're overlooking the massive impact of resenteeism and presenteeism on IT. For IT professionals tasked with managing the backbone of modern business operations, these numbers spell big trouble ...

For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...