Data Center Network Equipment
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Dell’Oro Group has raised its worldwide data center capex outlook for 2026 as hyperscale AI deployments accelerated, complemented by continued investments in general-purpose infrastructure and rising component costs.
“Rising memory and storage pricing substantially increased overall server system costs in the quarter and will likely remain a major capex growth factor this year,” said Baron Fung, Senior Research Director at Dell’Oro Group. “At the same time, AI infrastructure deployments continue to accelerate rapidly, while hyperscalers also expanded general-purpose infrastructure to support public cloud growth, agentic AI workloads, and rising AI-related storage requirements.
“Despite exceptionally strong spending growth in 1H2026, capex growth is expected to accelerate further in 2H26, driven by the ramp of NVIDIA Rubin systems and refresh cycles for hyperscaler custom accelerator platforms. Beyond hyperscalers, select enterprise verticals and sovereign cloud providers are increasing AI infrastructure adoption, though growth remains constrained by uncertain returns and infrastructure readiness. While near-term demand remains healthy, some spending may have been pulled forward ahead of expected price increases later this year,” explained Fung.
Additional highlights from the 1Q 2026 Data Center IT Capex Quarterly Report:
- The global data center capex outlook was raised to more than $1 trillion for 2026.
- The Top 4 U.S. cloud providers—Amazon, Google, Meta, and Microsoft—increased data center capex by 78%.
- Dell led server OEM revenue in the quarter, followed by SuperMicro and Lenovo, while white-box vendors serving the hyperscale market accounted for the majority of server revenue. Nearly all server vendors benefited from higher memory-driven system pricing.
Image Credit: Futurum Group
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Dell’Oro Group’s Data Center IT Capex Quarterly Report details the data center infrastructure capital expenditures of each of the ten largest Cloud service providers, as well as the Rest-of-Cloud, Telco, and Enterprise customer segments. Allocation of the data center infrastructure capex for general-purpose and accelerated servers, storage systems, and other auxiliary data center equipment is provided. The report also discusses market trends, drivers of the leading Cloud service providers’ capex growth during the quarter, and the outlook for the next year. To purchase this report, please contact us at [email protected].
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Perplexity.ai generated- June 2026 forecast:
Approximately 75% of hyperscaler capex in 2026 is for AI infrastructure (~$450 billion).
Key Drivers of the Forecast Increase:
Additional Secondary Factors:
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NVIDIA Rubin system ramp: Capex growth expected to accelerate further in 2H26 driven by Rubin system ramp
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Hyperscaler custom accelerator refresh cycles: Refresh cycles for custom accelerator platforms will drive 2H26 growth
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Enterprise verticals & sovereign cloud adoption: Select enterprise verticals and sovereign cloud providers increasing AI infrastructure adoption
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Pulled-forward spending: Some spending pulled forward ahead of expected price increases later in 2026
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References:
https://futurumgroup.com/insights/ai-capex-2026-the-690b-infrastructure-sprint/
https://know.creditsights.com/insights/technology-hyperscaler-capex-2026-estimates/
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
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Alphabet’s 2026 capex forecast soars; Gemini 3 AI model is a huge success
Hyperscaler capex > $600 bn in 2026 a 36% increase over 2025 while global spending on cloud infrastructure services skyrockets
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Inside Amazon’s new data center network architecture: quasi random network topology and passive optical devices
Amazon Web Services (AWS) claims it recently achieved a major breakthrough in Data Center Network (DCN) architecture and has been quietly deploying the new technology in its data centers since late last year. Amazon detailed its new networking design in a paper published May 21st titled “RNG: Flat Data Center Networks at Scale.” RNG, or “resilient network graphs,” is built around a quasi-random topology and new passive optical hardware. It’s a “quasi-random” design that combines elements of traditional, structured data networks with the performance advantages of more random architectures.
The goal is to move off conventional hierarchical “fat-tree” designs toward a flatter, more mesh-like fabric that uses far fewer routers and switches, offers more parallel paths, and therefore delivers higher effective throughput at lower power and capex.
“By essentially flattening the network, we eliminated the bottlenecks that come with traditional networking designs,” Matt Rehder, vice president of AWS Network Engineering, said in an exclusive interview with WIRED. “We think we’re the only ones who have done this at scale. RNG is a great fit for our core demands, but AI training data patterns are far more coordinated and centrally orchestrated, so they don’t approximate a random graph.”
The fact that Amazon is using this in the real world is “remarkable,” said Brighten Godfrey, a computer science professor at the University of Illinois Urbana-Champaign and an expert in networking, who was not involved in Amazon’s research. Godfrey coauthored a seminal 2012 paper on random network graphs, which he says are a “mind-bending problem to solve, in general.”
Classic cloud DCNs use structured topologies (Clos/fat-tree) where paths are highly regular and layered (Top of Rack (ToR)–aggregation–core). By contrast, random-graph theory says the most efficient routing networks are flat random graphs: each node connects to a small random subset of others, creating many short, diverse paths and graceful degradation under failures. The problem has always been practical: random cabling at scale is unmanageable, and routing across a huge random graph is nontrivial.
AWS’s “quasi-random” design essentially mixes determinism with randomness: key structural elements are fixed to keep the cabling and deployment manageable, while enough randomness is retained in the interconnect pattern to get the performance and resilience benefits of random graphs. The physical enabler is a new passive optical device called a ShuffleBox that standardizes how switches connect and internally permutes links so that, when many ShuffleBoxes are wired together, the resulting global topology is quasi-random without having to hand-design every link.
Image Credit: Amazon
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Key architectural pieces and claimed gains:
AWS reports that RNG-based fabrics now serve as the default network architecture for most new AWS data centers, after initial deployments beginning in 2024. The company claims the design:
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Uses roughly 69% fewer routers/switches than traditional fat-tree DCNs, because the network is flatter and relies more on passive optical fanout.
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Delivers up to about 33% higher throughput, due to more independent paths and better load spreading.
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Cuts network equipment power consumption by on the order of 40%, with associated reductions in cooling and operational overhead.
On the control-plane side, AWS developed a routing scheme called Spraypoint. Instead of always following a strict shortest path from source to destination, Spraypoint first “sprays” traffic randomly to neighbors, then directs it via preselected “waypoints” using more conventional shortest-path routing. This hybrid behavior exploits the quasi-random topology to open many more independent paths than standard ECMP-style shortest-path routing would, which in turn improves utilization and resilience under congestion or failures.
Strategic implications:
For AWS’s cloud and AI build-out, this is positioned as a foundational infrastructure advantage: higher bisection bandwidth and lower network energy per bit directly benefit large-scale AI training clusters, storage backends, and multi-tenant cloud workloads. Fewer active devices and more passive optics also translate into lower capex and opex at hyperscale, so AWS is framing this as both a performance and cost/sustainability play that could save billions of dollars and reduce CO₂ emissions over time.
From a networking-theory standpoint, this is notable as one of the first reported at-scale, production deployments of a flat random-graph-inspired topology in a hyperscale DCN, rather than a purely academic or lab system.
In a quasi-random topology like AWS’s RNG fabric, the impact on latency and jitter comes from three main effects: path length distribution, load spreading, and failure behavior.
Baseline latency: path lengths and device count:
In a traditional Clos/fat-tree, average latency is dominated by a fixed number of stages (ToR → agg → core → agg → ToR), so hop count is tightly controlled but you pay for many active devices. A quasi-random, flat graph replaces that rigid hierarchy with many short, irregular paths; on average, shortest paths between any two switches are similar or slightly shorter in hop count than in a fat-tree, and there are fewer active routers in the path because the architecture offloads fanout to passive optics. That tends to keep or slightly reduce median/mean latency per flow, especially under moderate load, because packets traverse fewer serialized queueing points even if the physical graph looks “messier.”
Jitter: congestion and path diversity:
Jitter is driven much more by variable queueing delay than by fixed propagation or serialization. In a quasi-random fabric with many alternate paths and a load-balancing scheme like Spraypoint (random spray + waypoint-based shortest paths), flows can be spread more evenly across the network, reducing hot spots and thus reducing the variance of queueing delay across packets. That can lower jitter compared with a Clos under the same aggregate load, because the system is less likely to funnel many flows through the same few congested uplinks or spine devices.
However, because the routing intentionally uses many different paths, per-flow packet reordering becomes more likely unless constrained by per-flow hashing or waypointing, which can show up as effective jitter at higher layers. AWS’s description of Spraypoint suggests they mitigate this by using waypoints and policy to preserve some path structure, so you get the diversity benefits without unconstrained per-packet spraying.
Under failure and high load:
Where quasi-random really helps latency/jitter is under failure and partial congestion. In a Clos, link or spine failures can force large sets of flows to converge on a smaller subset of remaining equal-cost paths, driving up queueing delay and jitter nonlinearly. In a resilient random-graph-style fabric, node/edge failures simply remove a few edges from a highly connected graph; there are typically many alternative short paths, so the increase in hop count and queueing pressure is smaller and more diffuse. That tends to keep tail latency and jitter (P99, P99.9) better behaved, even if median latency looks similar to a Clos at low load.
So, qualitatively: median latency is roughly comparable to a well-designed Clos, sometimes better due to fewer active stages; jitter and tail latency should improve under realistic, bursty load and failure scenarios, provided the routing stack is designed to limit packet reordering.
Summary and Conclusions:
Quasi-random data center topologies like AWS’s RNG fabric replace rigid Clos/fat-tree hierarchies with a flatter, graph-like network that preserves short path lengths while dramatically increasing path diversity, which tends to hold median latency roughly steady or slightly better by reducing the number of active, queueing devices per path and offloading fanout to passive optics. They primarily improve jitter and tail latency by spreading flows across many alternative routes so congestion is less concentrated, making queueing delays less bursty and keeping P99/P99.9 behavior more stable under failures and hot spots, provided the routing layer (for example, AWS’s Spraypoint approach) constrains packet reordering through way pointing or per-flow consistency.
In conclusion, quasi-random fabrics are less about shaving a few microseconds off baseline latency and more about delivering more predictable end-to-end performance—especially for east–west, latency-sensitive cloud and AI workloads—by trading rigid structure for statistically robust, highly connected graphs that degrade more gracefully when links, nodes, or traffic patterns become pathological.
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References:
https://arxiv.org/pdf/2604.15261
https://www.wired.com/story/amazon-aws-ceo-matt-garman-ai-agents/
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Data Center Networking Market to grow at a CAGR of 6.22% during 2022-2027 to reach $35.6 billion by 2027
TMR: Data Center Networking Market sees shift to user-centric & data-oriented business + CoreSite DC Tour
Big Fiber’s $250M financing deal to buildout dark fiber routes for AI Data Center expansion
Executive Summary:
Big Fiber [1.] has secured $250 million in financing from Stonepeak and Caisse de dépôt et placement du Québec (CDPQ) to expand its dark fiber footprint and increase network capacity in response to accelerating hyperscaler and large-scale data center investments in AI-driven workloads.
Note 1. Sunnyvale, CA headquartered Big Fiber was previously known as Bandwidth IG, which was originally established in 2019 as a telecom and dark-fiber infrastructure company. The rebrand to BIG Fiber was announced on May 1, 2025 when the company described it as a shift to better reflect its focus on privately owned, newly constructed dark fiber networks. The company has built privately owned metro dark fiber networks from its inception, primarily in the SF Bay Area and the Greater Portland, OR and Atlanta, GA areas.
BIG Fiber structures its dark fiber portfolio around high‑strand‑count, single‑mode, low‑loss fiber deployed in purpose‑built, underground metro and regional routes, rather than a carrier‑specific “technology” stack of its own. The company’s public materials emphasize:
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Single‑mode fiber (SMF) for metro and long‑haul connectivity, consistent with standard dark‑fiber infrastructure designed for multi‑wavelength and DWDM‑based upgrades.
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High‑density, high‑fiber‑count cables in metro corridors (often hundreds of strands) to support dense data‑center and interconnect demand, which is typical of “new‑build” dark‑fiber operators entering AI‑and‑cloud‑centric markets.
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Point‑to‑point and ring‑style topologies engineered for extreme route diversity (tri‑/quad‑versity) and low latency, rather than a legacy long‑haul backbone that relies on older fiber types or managed wavelengths.
To complement Big Fiber’s dark‑fiber infrastructure; the customer provides the optical PHY layer (e.g., coherent DWDM, 400ZR/ZR+, or other high‑speed optics), which is how dark‑fiber providers typically position their offerings.
–>More about Big Fiber at the end of this article from the company itself.
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Proceeds of the facility will be used to refinance existing debt, provide new capital and facilitate the necessary headroom for major fiber optic network expansions already underway. This includes a significant multi-market buildout in Greater Atlanta, adding over 205 route miles and 165,000 fiber miles to BIG Fiber’s existing market-leading footprint.
“Our partnership with Stonepeak Credit and La Caisse marks a pivotal moment in our mission to empower our customers with highly scalable and purpose-built dark fiber solutions,” said Bruce Garrison, CEO of BIG Fiber. “This financing ensures we have the scale to stay ahead of the escalating demand for modernized infrastructure enabling the AI ecosystem and the necessary digital highways for decades to come.”
“BIG Fiber’s infrastructure delivers critical bandwidth to meet the insatiable demand for both data and compute capacity across its key markets,” said Arun Varanasi, Managing Director at Stonepeak Credit. “We are proud to partner with Columbia Capital, SDC Capital Partners, and La Caisse to support the company’s next leg of growth as it positions itself as one of the preeminent dark fiber operators in the country.”
“BIG Fiber is well positioned to meet the growing connectivity needs of enterprises and data centers seeking new, high-quality infrastructure options,” said Jérôme Marquis, Managing Director and Head of Private Credit at La Caisse. “Its resilient business model, underpinned by long-term contracts and strong structural demand, positions the company well for growth. Together with Stonepeak Credit, we’re providing a tailored financing solution that supports the continued buildout of essential digital infrastructure.”
The latest expansion will bring BIG Fiber’s Atlanta and San Francisco Bay Area network capacity to 850 route miles and over 3 million fiber miles. Projects are currently under construction or contract, with phased Ready for Service (RFS) dates expected in early 2027.
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According to Big Fiber Chief Commercial Officer Patton Lochridge, demand signals are particularly strong in key U.S. metros including the San Francisco Bay Area, Hillsboro, and Atlanta, where new fiber routes are being deployed to support AI-centric data center expansion. “We’re seeing customers require extreme route diversity, often moving toward triversity or quadversity networks to connect metro assets and long-haul routes,” Lochridge said. He added that inference workloads are increasing the demand for dense metro connectivity: “Traditional telecommunications networks are often too congested or lack the latency and loss tolerances required for stringent AI workloads, making purpose-built metro fiber essential.” Lochridge indicated that the majority of the new capital will be directed toward greenfield build-outs and targeted overbuilds of “exhausted legacy telecommunications corridors that need more scale.”
Industry analysts highlight a parallel geographic shift in AI infrastructure deployment. Sterling Perrin, senior principal analyst for optical networks and transport at Omdia, noted that AI campuses are expanding beyond traditional connectivity hubs such as Ashburn, Dallas, and Northern California into power-advantaged regions including West Texas, Ohio, Tennessee, Louisiana, and Georgia. “They all require massive fiber optic connectivity,” Perrin said.
Power availability is emerging as a primary constraint shaping network topology. Ron Westfall, vice president and analyst at HyperFrame Research, emphasized that grid limitations are driving hyperscalers toward distributed AI campus architectures interconnected via metro and long-haul dark fiber. “Power grid constraints have forced a material shift toward metro and long-haul dark fiber infrastructure to stitch together distributed regional data center campuses,” Westfall said. “Because this relentless GPU-to-GPU communication demands near-zero latency and unprecedented bandwidth, infrastructure planners are prioritizing the deployment of ultra-high-strand dark fiber corridors that directly link distributed, power-rich data centers.”
AI Workloads Reshape Optical Demand:
AI-driven traffic growth is now materially impacting the optical supply chain. In its April 2026 post-OFC analysis, CRU Group reported that AI-related data center demand “has overtaken traditional telecom as the primary growth engine for optical [fiber] and cable,” contributing to tightening supply conditions for high-fiber-count cables and upstream preform materials.
Despite this surge, the majority of AI traffic remains intra-data-center. Omdia estimates indicate that up to 90% of AI traffic does not exit the facility during GPU cluster operations. However, the emergence of distributed AI architectures is beginning to increase requirements for high-capacity inter-data-center interconnect (DCI).
At the Optica Executive Forum, Cisco SVP and Fellow Rakesh Chopra highlighted the scale differential between AI and conventional traffic profiles. As cited by Perrin, AI “scale-up” traffic within data centers can generate 504 times more traffic than traditional DCI flows, while “scale-out” traffic can produce 56 times DCI bandwidth requirements. “With AI training models at the limits of what can be processed within a data center, distributed AI clusters are inevitable,” Perrin said.
This architectural transition is reflected in NVIDIA’s AI factory designs, which decouple east-west GPU compute traffic from traditional north-south enterprise flows, leveraging low-latency leaf-spine topologies optimized for continuous GPU synchronization.
Westfall further noted that these evolving traffic patterns are fundamentally altering network design assumptions. Operators are increasingly optimizing for persistent machine-to-machine synchronization rather than burst-oriented enterprise traffic models.
Fiber as a Core AI Infrastructure Asset:
The Big Fiber’s latest financing aligns with broader trends in AI infrastructure investment, where capital is being deployed across integrated stacks including energy, land, connectivity, and compute infrastructure. Utilities are expanding transmission capacity, while developers are co-locating generation resources near emerging AI hubs.
Within this context, fiber infrastructure is being revalued based on its strategic proximity to power-rich data center clusters. “Infrastructure monetization is shifting away from historical metrics such as per-megabit pricing toward asset-level valuations built around proximity to power-rich data centers,” Westfall said.
If current deployment trajectories persist, the resulting topology will consist of a dense, high-capacity mesh of metro and long-haul fiber routes interconnecting geographically distributed, power-optimized AI campuses with hyperscale cloud and interconnection ecosystems.
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About BIG Fiber:
BIG Fiber is a metro dark fiber provider that offers high capacity, strategically placed, dark fiber networks to mission critical data centers, Hyperscalers and enterprises throughout the San Francisco Bay Area, Greater Portland and Greater Atlanta areas. BIG Fiber’s 100% underground network meets critical data needs for enterprises and data centers that require new, quality infrastructure options. BIG Fiber’s San Francisco Bay Area network offers more than 320 route miles and 65 data centers. The Greater Portland network has more than 20 route miles and 15 data centers, and the Greater Atlanta network has more than 550 route miles and 30 data centers. BIG Fiber was founded in 2019 and is headquartered in Sunnyvale, California. Visit www.bigfiber.com to learn more.
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References:
BIG Fiber Secures $250 Million Financing Led by Stonepeak Credit and La Caisse
Analysis: Fiber Broadband Association (FBA) whitepaper: Upgrading MSO Networks to Fiber to the Home (FTTH): A Technical Perspective
Fiber Broadband Association Middle Mile WG: how to use “Digital Infrastructure Networks” for coordinated fiber backbone investments
Analysis: AT&T 1Q-2026 results: increased fiber penetration, FWA momentum, D2D deals, and mobile/home internet bundles
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
Fiber Optic Networks & Subsea Cable Systems as the foundation for AI and Cloud services
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Automating Fiber Testing in the Last Mile: An Experiment from the Field
AI wireless and fiber optic network technologies; IMT 2030 “native AI” concept
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
Executive Summary:
In a February 6, 2026 CNBC interview with with Scott Wapner, Nvidia CEO Jensen Huang [1.] characterized the current AI build‑out as “the largest infrastructure buildout in human history,” driven by exceptionally high demand for compute from hyperscalers and AI companies. “Through the roof” is how he described AI infrastructure spending. It’s a “once-in-a-generation infrastructure buildout,” specifically highlighting that demand for Nvidia’s Blackwell chips and the upcoming Vera Rubin platform is “sky-high.” He emphasized that the shift from experimental AI to AI as a fundamental utility has reached a definitive inflection point for every major industry.
Jensen forecasts aa roughly 7–to- 8‑year AI investment cycle lies ahead, with capital intensity justified because deployed AI infrastructure is already generating rising cash flows for operators. He maintains that the widely cited ~$660 billion AI data center capex pipeline is sustainable, on the grounds that GPUs and surrounding systems are revenue‑generating assets, not speculative overbuild. In his view, as long as customers can monetize AI workloads profitably, they will “keep multiplying their investments,” which underpins continued multi‑year GPU demand, including for prior‑generation parts that remain fully leased.
Note 1. Being the undisputed leader of AI hardware (GPU chips and networking equipment via its Mellanox acquisition), Nvidia MUST ALWAYS MAKE POSITIVE REMARKS AND FORECASTS related to the AI build out boom. Reader discretion is advised regarding Huang’s extremely bullish, “all-in on AI” remarks.
Huang reiterated that AI will “fundamentally change how we compute everything,” shifting data centers from general‑purpose CPU‑centric architectures to accelerated computing built around GPUs and dense networking. He emphasizes Nvidia’s positioning as a full‑stack infrastructure and computing platform provider—chips, systems, networking, and software—rather than a standalone chip vendor. He accuratedly stated that Nvidia designs “all components of AI infrastructure” so that system‑level optimization (GPU, NIC, interconnect, software stack) can deliver performance gains that outpace what is possible with a single chip under a slowing Moore’s Law. The installed base is presented as productive: even six‑year‑old A100‑class GPUs are described as fully utilized through leasing, underscoring persistent elasticity of AI compute demand across generations.
AI Poster Childs – OpenAI and Anthropic:
Huang praised OpenAI and Anthropic, the two leading artificial intelligence labs, which both use Nvidia chips through cloud providers. Nvidia invested $10 billion in Anthropic last year, and Huang said earlier this week that the chipmaker will invest heavily in OpenAI’s next fundraising round.
“Anthropic is making great money. Open AI is making great money,” Huang said. “If they could have twice as much compute, the revenues would go up four times as much.”
He said that all the graphics processing units that Nvidia has sold in the past — even six-year old chips such as the A100 — are currently being rented, reflecting sustained demand for AI computing power.
“To the extent that people continue to pay for the AI and the AI companies are able to generate a profit from that, they’re going to keep on doubling, doubling, doubling, doubling,” Huang said.
Economics, utilization, and returns:
On economics, Huang’s central claim is that AI capex converts into recurring, growing revenue streams for cloud providers and AI platforms, which differentiates this cycle from prior overbuilds. He highlights very high utilization: GPUs from multiple generations remain in service, with cloud operators effectively turning them into yield‑bearing infrastructure.
This utilization and monetization profile underlies his view that the capex “arms race” is rational: when AI services are profitable, incremental racks of GPUs, network fabric, and storage can be modeled as NPV‑positive infrastructure projects rather than speculative capacity. He implies that concerns about a near‑term capex cliff are misplaced so long as end‑market AI adoption continues to inflect.
Competitive and geopolitical context:
Huang acknowledges intensifying global competition in AI chips and infrastructure, including from Chinese vendors such as Huawei, especially under U.S. export controls that have reduced Nvidia’s China revenue share to roughly half of pre‑control levels. He frames Nvidia’s strategy as maintaining an innovation lead so that developers worldwide depend on its leading‑edge AI platforms, which he sees as key to U.S. leadership in the AI race.
He also ties AI infrastructure to national‑scale priorities in energy and industrial policy, suggesting that AI data centers are becoming a foundational layer of economic productivity, analogous to past buildouts in electricity and the internet.
Implications for hyperscalers and chips:
Hyperscalers (and also Nvidia customers) Meta , Amazon, Google/Alphabet and Microsoft recently stated that they plan to dramatically increase spending on AI infrastructure in the years ahead. In total, these hyperscalers could spend $660 billion on capital expenditures in 2026 [2.] , with much of that spending going toward buying Nvidia’s chips. Huang’s message to them is that AI data centers are evolving into “AI factories” where each gigawatt of capacity represents tens of billions of dollars of investment spanning land, compute, and networking. He suggests that the hyperscaler industry—roughly a $2.5 trillion sector with about $500 billion in annual capex transitioning from CPU to GPU‑centric generative AI—still has substantial room to run.
Note 2. An understated point is that while these hyperscalers are spending hundered of billions of dollars on AI data centers and Nvidia chips/equipment they are simultaneously laying off tens of thousands of employees. For example, Amazon recently announced 16,000 job cuts this year after 14,000 layoffs last October.
From a chip‑level perspective, he argues that Nvidia’s competitive moat stems from tightly integrated hardware, networking, and software ecosystems rather than any single component, positioning the company as the systems architect of AI infrastructure rather than just a merchant GPU vendor.
References:
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
184K global tech layoffs in 2025 to date; ~27.3% related to AI replacing workers
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
It’s no surprise to IEEE Techblog readers that Cisco’s networking business – still its biggest unit, generating nearly half its total sales – reported <$6.9 billion in revenue for the three-month period ending in January (Cisco’s second fiscal quarter). That was down 3% compared with the same quarter the year before. For its first half year, networking sales dropped 14% year-over-year, to about $13.6 billion.
However, total second-quarter revenues grew 9% year-over-year, to just less than $14 billion, boosted by the Splunk (security company) acquisition in March 2024. Thanks to that deal, Cisco’s security revenues more than doubled for the first half, to about $4.1 billion. But net income fell 8%, to roughly $2.4 billion, due partly to higher costs for research and development, as well as sales and marketing expenses.
Cisco groused about an “inventory correction” as networking customers digested stock they had already bought, but that surely is not the case now as that inventory has been worked off by its customers (ISPs, telcos, enterprise & government end users). Cisco CFO Richard Scott Herren now says “The demand that we’re seeing today a function of extended lead times like we saw a couple of years ago. That’s not the case. Our lead times are not extending.”
Currently, Cisco firmly believes that Ethernet connectivity sales to owners of AI data centers is an “emerging opportunity.” That refers to Cisco’s data center switching solutions for “web-scale” and enterprise customer intra-data center communications. The company’s AI strategy is described here.
Image Courtesy of Cisco Systems
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AI investments “will lead to our networking equipment being combined with Nvidia GPUs, and that’s how we’ll accomplish that in the future,” CEO Chuck Robbins told industry analysts on a call to discuss second-quarter results, according to a Motley Fool transcript. “There’s so much change going on right now from a technology perspective that there’s both excitement about the opportunity, and candidly, there’s a little bit of fear of slowing down too much and letting your competition get too much ahead of you. So, we saw solid demand,” he said.
However, Cisco will face mighty competition in that space.
- Nokia is targeting the same opportunity and last month said it would spend an additional €100 million (US$104 million) on its Internet Protocol unit annually with the goal of generating another €1 billion ($1.04 billion) in data center revenues by 2028.
- Arista Networks is another rival in this market, selling high performance Ethernet switches to cloud service providers like Microsoft.
- Nvidia, whose $7 billion acquisition of Mellanox in 2019 gave it effective control of InfiniBand, an alternative to Ethernet that had represented the main option for connecting GPU clusters when analysts published research on the topic in August 2023. Just as important, the Mellanox division of Nvidia also is a leader in Ethernet connectivity within data centers as described in this IEEE Techblog post.
- Juniper Networks (being acquired by HPC) is also focusing on networking the AI data center as per a white paper you can download after filling out this form.
During the Q & A, Robbins elaborated: “On the $700 million in AI orders, it’s a combination of systems, silicon, optics, and optical systems. And I think if you break it down, it’s about half is in silicon and systems. And it continues to accelerate. And I’d say the teams have done a great job on the silicon front. We’ve invested heavily in more resources there. The team is running parallel development efforts for multiple chips that are staggered in their time frames. They’ve worked hard. They were increasing the yield, which is a positive thing. And so, we feel good about it, but it’s a combination of all those things that we’re selling to the customers.”
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Enterprise AI:
“What we’re seeing on the enterprise side relative to AI is it’s still — customers are still in the very early days, and they all realize they need to figure out exactly what their use cases are. We’re starting to see some spending though on specific AI-driven infrastructure. And we think as we get AI pods out there — we got Hyperfabric coming. We got AI defense coming.
We have Hypershield in the market. And we got this new DPU switch, they are all going to be a part of the infrastructure to support these AI applications. So, we’re beginning to see it happen, but I think it’s also really important to understand that as the enterprises leverage their private data, their proprietary data, and they’ll do some training on that and then they’ll run inference obviously against that. We believe that opportunity is an order of magnitude higher than what we’ve seen in training today. We’re going to continue to innovate and build capabilities to put ourselves in a better position to be a real beneficiary as this continues to accelerate. But as of today, we feel like we’re in pretty good shape.”
“If you look at AI defense with the AI Summit that we did recently, there’s — I think there’s about 20-some-odd customers who are interested in going to proof of concept with us right now on it. We had almost half the Fortune 100 there for that event. So, I feel good about where we are. It will turn into greater demand as we just continue to scale these products.”
Telco use of AI Edge Applications:
“We see some of the European network operators are looking at delivering AI as a service,” said Robbins. “We see a lot of them planning for AI edge applications that are sitting at the edge of their networks that they’re managing for customers.”
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Cisco raised its guidance and now expects revenues for the full year of between $56 billion and $56.5 billion, up from its earlier range of $55.3 billion to $56.3 billion.
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References:
https://www.cisco.com/site/uk/en/solutions/artificial-intelligence/index.html
https://www.juniper.net/content/dam/www/assets/white-papers/us/en/networking-the-ai-data-center.pdf
Nokia selects Intel’s Justin Hotard as new CEO to increase growth in IP networking and data center connections
Initiatives and Analysis: Nokia focuses on data centers as its top growth market
Nvidia enters Data Center Ethernet market with its Spectrum-X networking platform
Initiatives and Analysis: Nokia focuses on data centers as its top growth market
Telco is no longer the top growth market for Nokia. Instead, it’s data centers, said Nokia’s CEO Pekka Lundmark on the company’s Q3 2024 earnings call last week. “Across Nokia, we are investing to create new growth opportunities outside of our traditional communications service provider market,” he said. “We see a significant opportunity to expand our presence in the data center market and are investing to broaden our product portfolio in IP Networks to better address this. There will be others as well, but that will be the number one. This is obviously in the very core of our strategy.”
Lundmark said Nokia’s telco total addressable market (TAM) is €84 billion, while its data center total addressable market is currently at €20 billion. “I mean, telco TAM will never be a significant growth market,” he added to no one’s surprise.
Nokia’s recent deal to acquire fiber optics equipment vendor Infinera for $2.3 Billion might help. The Finland based company said the combination with Infinera is expected to accelerate its path to double-digit operating margins in its optical-networks business unit (which was inherited from Alcatel-Lucent) . The transaction (expected to close in the first half of 2025) and the recent sale of submarine networks will reshape Nokia’s Network Infrastructure business to be built around fixed networks, internet-protocol networks and optical networks, the company said. Data centers not only require GPUs, but they also require optical networking to support their AI workloads. Lundmark said the role of optics will increase, not only in connections between data centers, but also inside data centers to connect servers to each other. “Once we get there, that market will be of extremely high volumes,” he said.
Pekka Lundmark, Nokia CEO– Photo: Arno Mikkor
- In September, Nokia announced the availability of its AI era, Event-Driven Automation (EDA) platform. Nokia EDA raises the bar on data center network operations with a modern approach that builds on Kubernetes to bring highly reliable, simplified, and adaptable lifecycle management to data center networks. Aimed at driving human error in network operations to zero, Nokia’s new platform reduces network disruptions and application downtime while also decreasing operational effort up to 40%. Nokia says its new EDA platform helps data center operators reduce errors in network operations. Nokia said it hopes to remove the risk of human error and reduce network disruptions and application downtime.
- A highlight of the recent quarter is a September deal with self proclaimed “AI hyperscalar” CoreWeave [1.] which selected Nokia to deploy its IP routing and optical transport equipment globally as part of its extensive backbone build-out, with immediate roll-out across its data centers in the U.S. and Europe. Raymond James analyst Simon Leopold said the CoreWeave win was good for Nokia to gain some exposure to AI, and he wondered if Nokia had a long-term strategy of evolving customers away from its telco base into more enterprise-like opportunities. “The reason why CoreWeave is so important is that they are now the leading GPU-as-a- service company,” said Lundmark. “And they have now taken pretty much our entire portfolio, both on the IP side and optical side. And as we know, AI is driving new business models, and one of the business models is clearly GPU-as-a-service,” he added.
Note 1. CoreWeave rents graphical processing units (GPUs) to artificial intelligence (AI) developers. A modern, Kubernetes native cloud that’s purpose-built for large scale, GPU-accelerated workloads. Designed with engineers and innovators in mind, CoreWeave claims to offer unparalleled access to a broad range of compute solutions that are up to 35x faster and 80% less expensive than legacy cloud providers.
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Nokia says its IP Interconnection can provide attractive business benefits to data center customers including:
- Improved security – Applications and services can be accessed via private direct connections to the networks of cloud providers collocated in the same facility without traversing the internet.
- Reduced transport costs – Colocated service providers, alternative network providers and carrier neutral network operators offer a wide choice of connections to remote destinations at a lower price.
- Higher performance and lower latency – As connections are direct and are often located closer to the person or thing they are serving, there is a reduction in latency and an increase in reliability as they bypass multiple hops across the public internet.
- More control – Through network automation and via customer portals, cloud service providers can gain more control of their cloud connectivity.
- Greater flexibility – With a wider range of connectivity options, enterprises can distribute application workloads and access cloud applications and services globally to meet business demands and to gain access to new markets.
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Nokia’s Data Center Market Risks:
The uncertainty is whether spending on GPUs and optical network equipment in the data center will produce the traffic growth to justify a decent ROI for Nokia. Also, the major cloud vendors (Amazon, Google, Microsoft and Facebook) design, develop, and install their own fiber optic networks. So it will likely be the new AI Data Center players that Nokia will try to sell to. William Webb, an independent consultant and former executive at Ofcom told Light Reading, “There may be substantially more traffic between data centers as models are trained but this will flow across high-capacity fiber connections which can be expanded easily if needed.” Text-based AI apps like ChatGPT generate “minuscule amounts of traffic,” he said. Video-based AI will merely substitute for the genuinely intelligent form.
References:
https://www.datacenterdynamics.com/en/news/nokia-eyes-data-center-market-growth-as-q3-sales-fall/
https://www.nokia.com/blog/enhance-cloud-services-with-high-capacity-interconnection/
https://www.lightreading.com/5g/telecom-glory-days-are-over-bad-news-for-nokia-worse-for-ericsson
AI adoption to accelerate growth in the $215 billion Data Center market
Market Overview:
Data Centers are a $215bn global market that grew 18% annually between 2018-2023. AI adoption is expected to accelerate data center growth as AI chips require 3-4x more electrical power versus traditional central processing units (CPUs).
AI adoption is poised to accelerate this growth meaningfully over coming years. BofA‘s US Semis analyst, Vivek Arya, forecasts the AI chip market to reach ~$200bn in 2027, up from $44bn in 2023. This has positive implications for the broader data center industry.
AI workloads are bandwidth-intensive, connecting hundreds of processors with gigabits of throughput. As these AI models grow, the number of GPUs required to process them grows, requiring larger networks to interconnect the GPUs. See Network Equipment market below.
The electrical and thermal equipment within a data center is sized for maximum load to ensure reliability and uptime. For electrical and thermal equipment manufacturers, AI adoption drives faster growth in data center power loads. AI chips require 3-4x more electrical power versus traditional CPUs (Central Processing Units).
BofA estimates data center capex was $215bn globally in 2023. The majority of this spend is for compute servers, networking and storage ($160bn) with data center infrastructure being an important, but smaller, piece ($55bn). For perspective, data center capex represented ~1% of global fixed capital formation, which includes all private & public sector spending on equipment and structures.
Networking Equipment Market:
BofA estimates a $20bn market size for Data Center networking equipment. Cisco is the market share leader, with an estimated 28% market share.
- Ethernet switches which communicate within the data center via local area networks. Typically, each rack would have a networking switch.
- Routers handle traffic between buildings, typically using internet protocol (IP). Some cloud service providers use “white box“ networking switches (e.g., manufactured by third parties, such as Taiwanese ODMs, to their specifications).
Data center speeds are in a state of constant growth. The industry has moved from 40G speeds to 100G speeds, and those are quickly giving way to 400G speeds. Yet even 400G speeds won’t be fast enough to support some emerging applications which may require 800G and 1.6TB data center speeds.
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Data Centers are also a bright spot for the construction industry. BofA notes that construction spending for data centers is approaching $30bn (vs $2bn in 2014) and accounts for nearly 21% of data center capex. At 4% of private construction spending (vs 2% five years ago), the data center category has surpassed retail, and could be a partial offset in a construction downturn.
Source: BofA Global Research
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References:
https://www.belden.com/blogs/smart-building/faster-data-center-speeds-depend-on-fiber-innovation#
Proposed solutions to high energy consumption of Generative AI LLMs: optimized hardware, new algorithms, green data centers
Nvidia enters Data Center Ethernet market with its Spectrum-X networking platform
Co-Packaged Optics to play an important role in data center switches
EdgeCore Digital Infrastructure and Zayo bring fiber connectivity to Santa Clara data center
Deutsche Telekom with AWS and VMware demonstrate a global enterprise network for seamless connectivity across geographically distributed data centers
Nvidia enters Data Center Ethernet market with its Spectrum-X networking platform
Nvidia is planning a big push into the Data Center Ethernet market. CFO Colette Kress said the Spectrum-X Ethernet-based networking solution it launched in May 2023 is “well on track to begin a multi-billion-dollar product line within a year.” The Spectrum-X platform includes: Ethernet switches, optics, cables and network interface cards (NICs). Nvidia already has a multi-billion-dollar play in this space in the form of its Ethernet NIC product. Kress said during Nvidia’s earnings call that “hundreds of customers have already adopted the platform.” And that Nvidia plans to “launch new Spectrum-X products every year to support demand for scaling compute clusters from tens of thousands of GPUs today to millions of DPUs in the near future.”
- With Spectrum-X, Nvidia will be competing with Arista, Cisco, and Juniper at the system level along with “bare metal switches” from Taiwanese ODMs running DriveNets network cloud software.
- With respect to high performance Ethernet switching silicon, Nvidia competitors include Broadcom, Marvell, Microchip, and Cisco (which uses Silicon One internally and also sells it on the merchant semiconductor market).
Image by Midjourney for Fierce Network
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In November 2023, Nvidia said it would work with Dell Technologies, Hewlett Packard Enterprise and Lenovo to incorporate Spectrum-X capabilities into their compute servers. Nvidia is now targeting tier-2 cloud service providers and enterprise customers looking for bundled solutions.
Dell’Oro Group VP Sameh Boujelbene told Fierce Network that “Nvidia is positioning Spectrum-X for AI back-end network deployments as an alternative fabric to InfiniBand. While InfiniBand currently dominates AI back-end networks with over 80% market share, Ethernet switches optimized for AI deployments have been gaining ground very quickly.” Boujelbene added Nvidia’s success with Spectrum-X thus far has largely been driven “by one major 100,000-GPU cluster, along with several smaller deployments by Cloud Service Providers.” By 2028, Boujelbene said Dell’Oro expects Ethernet switches to surpass InfiniBand for AI in the back-end network market, with revenues exceeding $10 billion.
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In a recent IEEE Techblog post we wrote:
While InfiniBand currently has the edge in the data center networking market, but several factors point to increased Ethernet adoption for AI clusters in the future. Recent innovations are addressing Ethernet’s shortcomings compared to InfiniBand:
- Lossless Ethernet technologies
- RDMA over Converged Ethernet (RoCE)
- Ultra Ethernet Consortium’s AI-focused specifications
Some real-world tests have shown Ethernet offering up to 10% improvement in job completion performance across all packet sizes compared to InfiniBand in complex AI training tasks. By 2028, it’s estimated that: 1] 45% of generative AI workloads will run on Ethernet (up from <20% now) and 2] 30% will run on InfiniBand (up from <20% now).
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References:
https://www.fierce-network.com/cloud/data-center-ethernet-nvidias-next-multi-billion-dollar-business
https://www.nvidia.com/en-us/networking/spectrumx/
Will AI clusters be interconnected via Infiniband or Ethernet: NVIDIA doesn’t care, but Broadcom sure does!
Data Center Networking Market to grow at a CAGR of 6.22% during 2022-2027 to reach $35.6 billion by 2027
LightCounting: Optical Ethernet Transceiver sales will increase by 40% in 2024
AI winner Nvidia faces competition with new super chip delayed
The Clear AI Winner Is: Nvidia!
Strong AI spending should help Nvidia make its own ambitious numbers when it reports earnings at the end of the month (it’s 2Q-2024 ended July 31st). Analysts are expecting nearly $25 billion in data center revenue for the July quarter—about what that business was generating annually a year ago. But the latest results won’t quell the growing concern investors have with the pace of AI spending among the world’s largest tech giants—and how it will eventually pay off.
In March, Nvidia unveiled its Blackwell chip series, succeeding its earlier flagship AI chip, the GH200 Grace Hopper Superchip, which was designed to speed generative AI applications. The NVIDIA GH200 NVL2 fully connects two GH200 Superchips with NVLink, delivering up to 288GB of high-bandwidth memory, 10 terabytes per second (TB/s) of memory bandwidth, and 1.2TB of fast memory. The GH200 NVL2 offers up to 3.5X more GPU memory capacity and 3X more bandwidth than the NVIDIA H100 Tensor Core GPU in a single server for compute- and memory-intensive workloads. The GH200 meanwhile combines an H100 chip [1.] with an Arm CPU and more memory.
Photo Credit: Nvidia
Note 1. The Nvidia H100, sits in a 10.5 inch graphics card which is then bundled together into a server rack alongside dozens of other H100 cards to create one massive data center computer.
This week, Nvidia informed Microsoft and another major cloud service provider of a delay in the production of its most advanced AI chip in the Blackwell series, the Information website said, citing a Microsoft employee and another person with knowledge of the matter.
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Nvidia Competitors Emerge – but are their chips ONLY for internal use?
In addition to AMD, Nvidia has several big tech competitors that are currently not in the merchant market semiconductor business. These include:
- Huawei has developed the Ascend series of chips to rival Nvidia’s AI chips, with the Ascend 910B chip as its main competitor to Nvidia’s A100 GPU chip. Huawei is the second largest cloud services provider in China, just behind Alibaba and ahead of Tencent.
- Microsoft has unveiled an AI chip called the Azure Maia AI Accelerator, optimized for artificial intelligence (AI) tasks and generative AI as well as the Azure Cobalt CPU, an Arm-based processor tailored to run general purpose compute workloads on the Microsoft Cloud.
- Last year, Meta announced it was developing its own AI hardware. This past April, Meta announced its next generation of custom-made processor chips designed for their AI workloads. The latest version significantly improves performance compared to the last generation and helps power their ranking and recommendation ads models on Facebook and Instagram.
- Also in April, Google revealed the details of a new version of its data center AI chips and announced an Arm-based based central processor. Google’s 10 year old Tensor Processing Units (TPUs) are one of the few viable alternatives to the advanced AI chips made by Nvidia, though developers can only access them through Google’s Cloud Platform and not buy them directly.
As demand for generative AI services continues to grow, it’s evident that GPU chips will be the next big battleground for AI supremacy.
References:
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
https://www.nvidia.com/en-us/data-center/grace-hopper-superchip/
https://www.theverge.com/2024/2/1/24058186/ai-chips-meta-microsoft-google-nvidia/archives/2
https://news.microsoft.com/source/features/ai/in-house-chips-silicon-to-service-to-meet-ai-demand/
Light Source Communications Secures Deal with Major Global Hyperscaler for Fiber Network in Phoenix Metro Area
Light Source Communications is building a 140-mile fiber middle-mile network in the Phoenix, AZ metro area, covering nine cities: Phoenix, Mesa, Tempe, Chandler, Gilbert, Queen Creek, Avondale, Coronado and Cashion. The company already has a major hyperscaler as the first anchor tenant.
There are currently 70 existing and planned data centers in the area that Light Source will serve. As one might expect, the increase in data centers stems from the boom in artificial intelligence (AI).
The network will include a big ring, which will be divided into three separate rings. In total, Light Source will be deploying 140 miles of fiber. The company has partnered with engineering and construction provider Future Infrastructure LLC, a division of Primoris Services Corp., to make it happen.
“I would say that AI happens to be blowing up our industry, as you know. It’s really in response to the amount of data that AI is demanding,” said Debra Freitas [1.], CEO of Light Source Communications (LSC).
Note 1. Debra Freitas has led LSC since co-founding in 2014. Owned and operated network with global OTT as a customer. She developed key customer relationships, secured funding for growth. Currently sits on the Executive Board of Incompas.
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Light Source plans for the entire 140-mile route to be underground. It’s currently working with the city councils and permitting departments of the nine cities as it goes through its engineering and permit approval processes. Freitas said the company expects to receive approvals from all the city councils and to begin construction in the third quarter of this year, concluding by the end of 2025.
Primoris delivers a range of specialty construction services to the utility, energy, and renewables markets throughout the United States and Canada. Its communications business is a leading provider of critical infrastructure solutions, including program management, engineering, fabrication, replacement, and maintenance. With over 12,700 employees, Primoris had revenue of $5.7 billion in 2023.
“We’re proud to partner with Light Source Communications on this impactful project, which will exceed the growing demands for high-capacity, reliable connectivity in the Phoenix area,” said Scott Comley, president of Primoris’ communications business. “Our commitment to innovation and excellence is well-aligned with Light Source’s cutting-edge solutions and we look forward to delivering with quality and safety at the forefront.”
Light Source is a carrier neutral, owner-operator of networks serving enterprises throughout the U.S. In addition to Phoenix, several new dark fiber routes are in development in major markets throughout the Central and Western United States. For more information about Light Source Communications, go to lightsourcecom.net.
The city councils in the Phoenix metro area have been pretty busy with fiber-build applications the past couple of years because the area is also a hotbed for companies building fiber-to-the-premises (FTTP) networks. In 2022 the Mesa City Council approved four different providers to build fiber networks. AT&T and BlackRock have said their joint venture would also start deploying fiber in Mesa.
Light Source is focusing on middle-mile, rather than FTTP because that’s where the demand is, according to Freitas. “Our route is a unique route, meaning there are no other providers where we’re going. We have a demand for the route we’re putting in,” she noted.
The company says it already has “a major, global hyperscaler” anchor tenant, but it won’t divulge who that tenant is. Its network will also touch Arizona State University at Tempe and the University of Arizona.
Light Source doesn’t light any of the fiber it deploys. Rather, it is carrier neutral and sells the dark fiber to customers who light it themselves and who may resell it to their own customers.
Light Source began operations in 2014 and is backed by private equity. It did not receive any federal grants for the new middle-mile network in Arizona.
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Bill Long, Zayo’s chief product officer, told Fierce Telecom recently that data centers are preparing for an onslaught of demand for more compute power, which will be needed to handle AI workloads and train new AI models.
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About Light Source Communications:
Light Source Communications (LSC) is a carrier neutral, customer agnostic provider of secure, scalable, reliable connectivity on a state-of-the-art dark fiber network. The immense amounts of data businesses require to compete in today’s global market requires access to an enhanced fiber infrastructure that allows them to control their data. With over 120 years of telecom experience, LSC offers an owner-operated network for U.S. businesses to succeed here and abroad. LSC is uniquely positioned and is highly qualified to build the next generation of dark fiber routes across North America, providing the key connections for business today and tomorrow.
References:
https://www.lightsourcecom.net/services/
https://www.fiercetelecom.com/ai/ai-demand-spurs-light-source-build-middle-mile-network-phoenix
Proposed solutions to high energy consumption of Generative AI LLMs: optimized hardware, new algorithms, green data centers
AI sparks huge increase in U.S. energy consumption and is straining the power grid; transmission/distribution as a major problem
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
