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Rami Rahim’s point is that AI infrastructure is only as useful as the network connecting its compute. When that network cannot deliver the bandwidth and performance GPU workloads demand, expensive accelerators may sit underused. HPE is consequently pitching networking as a strategic part of AI infrastructure, not merely the layer that connects servers.
What Rahim means by an “extraordinary” pace of change
At HPE Networking Investor Day, Rami Rahim, HPE’s executive vice president, president and general manager of HPE Networking, described AI as changing how data centers are designed and deployed. “The pace of change in the data centre is, quite frankly, extraordinary,” he said. “AI has fundamentally reshaped how infrastructure is designed and deployed.”
His explanation was practical: “You can invest billions of dollars into GPUs, but if the network can’t keep up with the traffic and the performance demands, those resources are not fully utilised.” For AI clusters, the network is part of the system’s performance envelope. Insufficient throughput or poor performance can constrain how effectively compute resources are used.
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Why AI makes networking a strategic infrastructure layer
AI workloads can generate substantial traffic among computing resources, making network capacity and latency important to overall cluster performance. That changes the investment calculation: adding accelerators alone does not ensure a capable AI system if the connections between them cannot carry the required traffic.
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Rahim’s argument is not that networking replaces compute. It is that the two must be planned together. A buyer evaluating an AI deployment should therefore consider the network’s ability to meet workload demands alongside the GPU and server specifications.
How HPE says its data-center networking business will grow
HPE’s figures are company expectations, not an independent forecast for the entire data-center networking market. They refer to HPE’s fiscal years, which should not be confused with calendar years.
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| Measure | HPE’s stated outlook | Attribution and period |
|---|---|---|
| AI-network orders | Expected to exceed $3 billion | HPE, cumulative orders expected for fiscal 2026; stated at HPE Networking Investor Day in 2026 |
| Data-center-networking revenue | Roughly 50% compound annual growth rate (CAGR) | HPE forecast through fiscal 2029, stated in 2026 |
| Networking revenue | High-teens to low-20s percentage growth range | HPE’s raised fiscal 2027 outlook, as reported by Reseller News on October 1, 2026 |
These figures describe different measures and periods: orders are not the same as recognized revenue, and the data-center networking forecast is a segment-specific growth expectation. The high-teens to low-20s outlook refers to HPE networking revenue, as reported by Reseller News.
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What HPE’s AI networking strategy includes
Ethernet and scale-up systems
HPE is positioning standards-based Ethernet for a more prominent role in the data-center stack. Rahim described capacity relationships using three terms: scale-across, scale-out and scale-up. His comparison was that scale-out is orders of magnitude greater than scale-across, while scale-up is orders of magnitude greater than scale-out. The point is that network design must account for multiple levels of communication as AI systems expand.
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HPE also pointed to an integrated AMD Helios rack solution with purpose-built HPE networking for scale-up switching and software. That links networking more closely to the rack-scale system rather than treating it as a separate afterthought. HPE’s description establishes the direction of its offering; it does not, by itself, specify product availability or customer deployment details.
Routing between buildings, campuses and geographies
HPE’s strategy also includes routing. As AI clusters span beyond a single data center to multiple buildings, campuses or geographies, operators need to connect those environments while addressing bandwidth, latency and power efficiency. The needs of links between sites are related to, but distinct from, the switching requirements inside a rack or cluster.
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Campus, branch and security portfolio
HPE’s broader networking plans extend beyond data centers. The company has linked campus and branch modernization to Wi-Fi 7 and described security capabilities spanning SASE, network access control, firewalls, identity-based policy enforcement and AI-powered operations. These offerings address enterprise network access and protection; they are adjacent to, rather than substitutes for, AI-cluster switching and routing.
What the Juniper acquisition changes
Rahim joined HPE after its acquisition of Juniper Networks and now leads HPE Networking. HPE’s profile says he was Juniper’s CEO for ten years before joining HPE. His role therefore places the integration of Juniper and Aruba networking businesses within the organization responsible for HPE’s networking strategy.
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SDxCentral reported that Rahim linked HPE’s improved guidance to accelerated data-center investment and growing confidence in post-acquisition execution. The report also cited more than $200 million in Helios networking-tray orders, a $1.2 billion Vultr deal, and a quarter with $2.9 billion in networking revenue and 75% year-over-year segment growth. The quarter’s period is not specified here, so those figures should be treated as the report’s snapshot rather than a current run rate or a forecast.
For customers, the strategic implication is portfolio integration: HPE is bringing Juniper and Aruba offerings under HPE Networking while positioning its products across AI data centers, routing, campus and branch networking, and security. The practical value will depend on how well those components meet a specific organization’s requirements and work together in its environment.
What to compare when evaluating HPE’s approach
HPE’s announcements describe a strategy, not a complete technical specification or a customer-specific recommendation. Buyers comparing networking approaches can use these criteria to assess whether a proposed design fits their workloads and operating needs:
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- Bandwidth by scale: Compare the requirements for scale-up and scale-out traffic, and ask how the design addresses communication at each level.
- Latency and power: Evaluate performance and energy requirements both within the cluster and across connected sites.
- Fabric and standards: Determine whether standards-based Ethernet fits the environment, and compare its integration demands with alternative fabric approaches.
- Multi-site routing: Assess how the network connects data centers, buildings, campuses or geographic locations.
- Operational integration: Examine how switching, routing, software, security and existing infrastructure will be managed together.
- Enterprise needs: Consider campus and branch modernization, access control, security policy and the channel or service model alongside AI infrastructure.
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