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Megaport raises A$827M to build a distributed AI cloud and chase the inference market
Four new contracts and a big GPU bet turn the Australian networking company into an AI-infrastructure play. Megaport spent a decade as a company you used to connect to other people's clouds. On Wednesday it announced a plan to become one. The Australian networking firm secured four new AI infrastructure contracts worth a combined A$458.9M (about $329M) and launched a fully underwritten entitlement offer to raise A$827.3M (about $594M), according to its filing. The money funds a pivot from plumbing to compute. The contracts come first. All four are with US-based technology providers running AI applications, are expected to start in the first half of 2027, and require nearly A$369.5M in capital expenditure, mostly for high-performance Nvidia GPUs alongside network and storage. That is a meaningful commitment for a company of Megaport's size, and it explains why the raise is so large relative to the business. What the capital is really buying is the strategy behind the contracts. Megaport says it will build a globally distributed AI inference cloud, anchored by an on-demand GPU pool backed by about A$350M in investment and offered to enterprise customers on both contracted and consumption-based pricing. The pool is to be deployed across the company's existing footprint of more than 1,100 connected data centres in 31 countries, with rollout over the next six to nine months. The bet is geographic. Most GPU capacity today sits in a handful of enormous data centres optimised for training the largest models. Megaport is targeting the other half of the AI workload: inference, the act of running a trained model to answer a query, which benefits from being close to the user. Its pitch is that a distributed network of smaller GPU pools, spread across the data centres it already connects, fits inference better than centralised mega-campuses, and slots into the gap between hyperscaler clouds and single-location GPU specialists. It is a credible reading of where AI infrastructure is heading. As models move from research demos into products embedded in real applications, the economics shift from training to serving, and serving rewards proximity and distribution. Megaport already owns the network that links the locations where that compute would live, which is a genuine structural advantage if the thesis holds. The numbers around the raise were briefly muddled across early coverage, which is worth untangling. The four contracts are worth A$458.9M in total contract value; the capital raise is A$827.3M; the GPU pool commitment is about A$350M. Several headlines collapsed these into a single figure. They are distinct: contract wins, the money to fund them, and the specific compute investment inside that money. Megaport also tightened its 2026 revenue guidance to A$307M-A$315M and projected combined group pro forma annual recurring revenue of A$662.9M once the compute division is folded in. The shares were halted while the raise was arranged, a standard mechanism for a deal of this scale on the ASX. The risk is the obvious one for any company spending heavily on Nvidia GPUs on the strength of contracts that begin in 2027: that AI infrastructure demand, and pricing, may look different by the time the hardware is installed and earning. Megaport is committing capital now against revenue that lands later, in a market moving fast enough that 18 months is a long time. The contracts give it a floor. The inference-cloud ambition is the part that has to compound, and that is the part the A$827M is really betting on.
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Australia's Megaport secures four new AI infrastructure contracts, to raise $594 million
The four contracts, all with U.S.-based technology providers running AI applications, are expected to start in the first half of 2027 and require nearly A$369.5 million in capital expenditure, primarily for high-performance Nvidia GPUs, network and storage infrastructure. Australia's Megaport said on Wednesday it has secured four new AI infrastructure contracts with a total contract value of about A$458.9 million ($329.49 million), and launched a fully underwritten entitlement offer to raise A$827.3 million ($594 million). The four contracts, all with U.S.-based technology providers running AI applications, are expected to start in the first half of 2027 and require nearly A$369.5 million in capital expenditure, primarily for high-performance Nvidia GPUs, network and storage infrastructure. Megaport said it would set up a globally distributed AI inference cloud, anchored by an on-demand GPU pool backed by A$350 million in investment, which will be offered to enterprise customers through contracted and consumption-based pricing models. "AI inference represents one of the biggest infrastructure opportunities of the next decade," Megaport CEO Michael Reid said. "As AI adoption accelerates, organisations need seamless access to GPUs, CPUs, storage, and the connectivity that powers them." The move marks a big step up in Megaport's push into AI infrastructure, with the company betting that demand for GPU-based compute will surge as enterprise AI adoption shifts from model training to latency-sensitive inference workloads. The firm, which uses Nvidia and AMD chips, said its network of more than 1,100 data centres in 31 countries puts it in a strong position to deliver AI compute closer to end users, addressing key bottlenecks including power, connectivity and access to high-performance GPUs. The entitlement offer, priced at A$14.30 per share, represents a 13.9% discount to Megaport's last closing price on June 1. Megaport also tightened its 2026 revenue guidance to A$307 million-A$315 million, reflecting strong momentum in its network business. Its previous expectation was between A$302 million and A$317 million.
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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, 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
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.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 months1
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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"2
.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.Related Stories
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 integrated1
.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.Summarized by
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