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
According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...
Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...
IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...
Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...
In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ...
In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...
In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...
In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...
In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...
From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...