HPE Juniper Networking has joined the Vultr Cloud Alliance, complementing Vultr’s AI-first cloud infrastructure with an AI-optimized Ethernet networking architecture that enhances AI throughput through advanced congestion management and load balancing capabilities. This partnership combines Vultr’s on-demand GPU clusters, bare metal isolation, Kubernetes support, predictable pricing, and global footprint with advanced data center networking to maximize GPU utilization under heavy AI traffic.
Together, HPE Juniper Networking and Vultr address networking-induced GPU bottlenecks that can cause packet loss and, ultimately, lead to wasted compute.
How HPE Juniper Networking fills a gap for modern AI
Unlike general-purpose cloud workloads that rely primarily on north-south traffic, large-scale AI workloads (especially large-scale ones) rely on east-west traffic for rapid data transfers across nodes and GPUs, using RDMA over Converged Ethernet (RoCEv2). Distributed AI training workloads generate tensors that move between GPUs, keeping models synchronized and progressing efficiently.
GPUs cannot operate at peak efficiency on their own: To work efficiently, they need a network capable of moving data at the same pace. Even small increases in latency or packet loss can lead to significant GPU stalling, wasted compute, and restricted scalability.
Backend fabrics must ensure:
- Lossless RDMA transport
- Deterministic latency under contention
- Stable throughput under sustained load
- Intelligent congestion management to prevent cascading failures
HPE Juniper Networking’s architecture and fabric are purpose-built for AI-scale Ethernet, meeting these requirements. The QFX5240 switch (powered by Broadcom Tomahawk 5 chips) delivers consistent, predictable RoCEv2 performance at scale, enabling efficient, high-performance AI deployments.
HPE Juniper Networking: Ethernet fabric for enhanced AI processing
HPE Juniper Networking’s fabric design provides advanced congestion control and load balancing to maintain efficiency amid heavy AI traffic. The QFX5240 switch supports backend, frontend, storage, and OOB networks, especially in rail-optimized backend fabrics for AI workloads.
Operating within a Data Center Quantized Congestion Notification (DCQCN) framework, the QFX5240 switch is optimized for RoCEv2 traffic, ensuring predictable GPU-to-GPU communication and AI training performance.
HPE Juniper Networking further enhances reliability through comprehensive AI/ML lab interoperability testing, with rigorously vetted architectures published as HPE Juniper Validated Designs (JVDs) and accompanied by detailed configuration guidance. This ensures that customers have access to proven AI/ML solutions for deployment and operation. Together, these capabilities reinforce HPE Juniper Networking’s ability to deliver predictable and reliable performance for AI cluster-scale workloads.
Both HPE Juniper Networking and Vultr are members of the Ethernet Alliance, reinforcing a shared commitment to open, standards-based Ethernet networking that promotes interoperability, flexibility, and scalable performance for AI-driven cloud and data center environments.
HPE Juniper Networking on Vultr infrastructure for repeatable, affordable deployment
Vultr’s AI infrastructure was built with a network-first design, making HPE Juniper Networking an ideal partner. Vultr provides the platform that operationalizes HPE Juniper Networking’s architecture, enabling reliable, global, scalable, high-performance AI with simplified deployment and repeatability.
Beyond providing reliable, affordable access to cloud GPUs, bare metal, cloud compute, and Kubernetes on Vultr, HPE Juniper Networking ensures these resources operate with maximum efficiency and utilization. Its JVDs dramatically reduce design and tuning risk, while Vultr’s easy-to-use platform supports production AI deployment patterns. Together, they minimize manual configuration and operational overhead, providing faster time-to-value for cluster-scale AI workloads.
Modern AI training and inference workloads aren’t limited by GPUs. They’re limited by network infrastructure that isn’t optimized for AI. The Vultr and HPE Juniper Networking partnership addresses this critical bottleneck, helping enterprises deploy large-scale AI clusters that perform at their full potential with confidence and predictability.
Get started today
Looking to combine Vultr’s composable cloud infrastructure with the networking that gets the most out of your cluster-scale AI workloads? Learn more about our partnership, or contact the Vultr sales team.