Megaport raises $594M to build distributed AI inference cloud across 1,100 data centers

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Australian networking company Megaport secured four new AI infrastructure contracts worth $329M with US technology providers and launched a $594M capital raise to pivot from network plumbing to compute. The company plans to build a globally distributed AI inference cloud using Nvidia GPUs across its existing footprint of over 1,100 data centers in 31 countries.

Megaport Pivots from Networking to AI Infrastructure

Megaport, an Australian networking company that spent a decade connecting enterprises to cloud providers, announced a major strategic shift on Wednesday. The firm secured four new AI infrastructure contracts with a total value of A$458.9M (approximately $329M) and launched a fully underwritten entitlement offer to raise A$827.3M (about $594M)

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. The capital raising represents a decisive pivot from network plumbing to compute infrastructure, transforming the company into an AI-infrastructure play.

Source: ET

Source: ET

All four contracts are with US-based AI technology providers running AI applications and are expected to start in the first half of 2027. The deals require nearly A$369.5M in capital expenditure for Nvidia GPUs, primarily for high-performance hardware alongside network and storage infrastructure

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. This represents a meaningful commitment for a company of Megaport's size, explaining why the entitlement offer is so substantial relative to the existing business.

Building a Globally Distributed AI Inference Cloud

The strategy behind the contracts centers on building a globally distributed AI inference cloud anchored by an on-demand GPU pool backed by approximately A$350M in investment

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. This distributed AI cloud will be offered to enterprise customers through both contracted and consumption-based pricing models, with deployment planned across the company's existing footprint of more than 1,100 connected data centers in 31 countries over the next six to nine months

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The bet is fundamentally geographic. While most GPU capacity today sits in a handful of enormous data centers optimized for training the largest models, Megaport is targeting the inference market—the act of running a trained model to answer queries, which benefits from proximity to users

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. CEO Michael Reid emphasized this opportunity, stating that "AI inference represents one of the biggest infrastructure opportunities of the next decade" and that "as AI adoption accelerates, organisations need seamless access to GPUs, CPUs, storage, and the connectivity that powers them"

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Strategic Positioning Between Hyperscalers and Specialists

Megaport's pitch positions a distributed network of smaller GPU pools spread across data centers as better suited for inference workloads than centralized mega-campuses. The company aims to occupy the gap between hyperscaler clouds and single-location GPU specialists

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. This represents a credible reading of where AI infrastructure is heading: as models move from research demonstrations into products embedded in real applications, the economics shift from training to serving, and serving rewards proximity and distribution.

The firm's network of more than 1,100 data centers in 31 countries positions it to deliver GPU-based compute closer to end users, addressing critical bottlenecks including power, connectivity, and access to high-performance GPUs

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. Megaport already owns the network linking the locations where this compute would live, creating a structural advantage if the thesis holds.

Financial Details and Market Risks

The entitlement offer is priced at A$14.30 per share, representing a 13.9% discount to Megaport's last closing price on June 1

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. The company also tightened its 2026 revenue guidance to A$307M-A$315M from a previous range of A$302M-A$317M, and projected combined group pro forma annual recurring revenue of A$662.9M once the compute division is integrated

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The primary risk is evident: Megaport is committing substantial capital expenditure for Nvidia GPUs based on contracts beginning in 2027, while AI infrastructure demand and pricing may shift significantly before the hardware is installed and generating revenue. The company is betting capital now against revenue that arrives later in a market moving fast enough that 18 months represents considerable uncertainty

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. The contracts provide a revenue floor, but the on-demand GPU capacity and inference-cloud ambition must compound for the A$827M bet to pay off. Watchers should monitor how enterprise AI adoption evolves from model training to latency-sensitive inference workloads, and whether distributed infrastructure gains traction over centralized alternatives.

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