The AI infrastructure race in the Asia-Pacific region is heating up, and SharonAI Holdings Inc. (NASDAQ: SHAZ) has secured one of its largest wins yet.
The Australian Neocloud provider announced a five-year cloud computing services agreement worth US$1.32 billion with an undisclosed global artificial intelligence lab. The contract will see Sharon AI deploy AI cloud infrastructure from its upcoming New Zealand AI Factory, with revenue expected to begin flowing during the first half of 2027.
While the customer remains unnamed—a common practice for large AI infrastructure agreements—the deal underscores the surging demand for high-performance AI compute as enterprises, research organizations, and AI developers compete for GPU capacity.
A Billion-Dollar Bet on AI Infrastructure
Under the agreement, Sharon AI will deliver cloud computing services through its New Zealand-based data center infrastructure, marking the company’s first AI Factory deployment in the country.
The expansion is part of Sharon AI’s broader strategy to build AI-native cloud infrastructure across the Asia-Pacific region. Unlike traditional cloud providers that support a broad range of workloads, Neocloud companies specialize in GPU-intensive environments designed specifically for training and deploying large language models, generative AI applications, and advanced machine learning workloads.
The announcement also highlights the scale Sharon AI is targeting. The company says its total AI Factory capacity now stands at 132 megawatts (MW), with 116 MW already contracted to customers. By mid-2027, Sharon AI expects to deploy more than 62,000 NVIDIA GPUs, creating one of the larger AI compute footprints in the region.
That level of GPU capacity places Sharon AI among a growing wave of infrastructure providers seeking to capitalize on the global AI boom, where access to accelerated computing has become one of the industry’s biggest competitive advantages.
Why New Zealand?
According to Co-founder and CEO James Manning, New Zealand offers both established data center infrastructure and long-term expansion opportunities, making it an attractive location for the company’s first AI Factory in the country.
Manning added that Sharon AI continues to see strong demand across enterprise customers, government agencies, hyperscalers, AI-native companies, and research institutions throughout Asia-Pacific and other global markets.
The move reflects a broader industry trend. As demand for AI computing continues to outpace available GPU supply, cloud providers are increasingly expanding into regions with reliable energy infrastructure, favorable regulatory environments, and room for large-scale data center growth.
The Neocloud Market Continues to Expand
Sharon AI’s latest contract arrives as specialized AI cloud providers challenge traditional hyperscalers by offering dedicated GPU infrastructure optimized for AI workloads. Companies including CoreWeave, Lambda, Crusoe, and several regional Neocloud providers have attracted significant customer demand as organizations seek alternatives to conventional cloud platforms.
Unlike general-purpose cloud services, AI Factories are purpose-built to deliver high-density GPU clusters, advanced networking, and optimized storage capable of supporting today’s increasingly large AI models.
The inclusion of more than 62,000 NVIDIA GPUs also signals the enormous infrastructure investments required to support next-generation foundation models and enterprise AI deployments. GPU shortages, once considered a temporary issue, remain a defining characteristic of the AI infrastructure market, making long-term compute agreements increasingly valuable for both providers and customers.
What It Means
The $1.32 billion agreement represents more than just a major revenue milestone for Sharon AI. It validates the growing role of regional AI infrastructure providers in a market historically dominated by global hyperscalers.
With most of its planned AI Factory capacity already committed before coming online, Sharon AI appears well-positioned to benefit from sustained demand for accelerated computing across enterprise, government, research, and AI-native organizations.
As AI adoption continues to expand globally, the companies building the infrastructure behind large-scale AI workloads may become just as strategically important as the organizations developing the models themselves.
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