The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
January 8, 2026 6:31 PM
The inside of a data centre, with rows of server racks. PHOTO: FREEPIK
As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.
Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.
At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.
Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.
This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.
The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.
Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.
Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.
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Six technology companies showcased solutions for financial research, wealth management, consumer insight and security to investors and institutions.
Updated
November 14, 2026 11:54 PM
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Arnold Lau and King Leung (back row, fourth and fifth from left), Associate Directors-General of Investment Promotion at InvestHK, join the Ventureport team and representatives from the six technology companies. PHOTO: VENTUREPORT
Ventureport hosted the soft launch of InvestHK’s Fast-Track Connect at its AI+ Tech+ Finance+ gathering on 30 October, bringing more than 100 guests to Pier 1929 in Wan Chai.
Sponsored by InvestHK, the event brought investors, institutional representatives and technology companies together to explore investment and business partnerships. Six companies took the stage to showcase software solutions for financial professionals, consumer research, local AI computing and digital asset security.
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The Acting Head of Startups, at InvestHK, Chin-yung (CY), and the Senior Vice President (Fintech), Pauline Fan, introduced Fast-Track Connect. They explained how the agency’s international network connects it with technology companies seeking investment, customers and business opportunities in Hong Kong.
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Their remarks also highlight the need for a more structured approach to build these connections. Investors need easier ways to identify companies that match their interests, while institutions seeking new technology need access to businesses whose products could support their operations or benefit their customers.
To address this need, Fast-Track Connect provides an online platform where technology companies can publish profiles outlining their products and funding needs. Investors and corporate participants can identify relevant businesses and contact them to explore potential investments or collaborations. Service providers and community partners are also part of the platform. Alongside investors and corporate participants, these groups are designated as Champions on the platform.
Curated by InvestHK and supported by the Financial Services and the Treasury Bureau, Fast-Track Connect builds on the Global Fast Track Programme. It aims to help local enterprises expand into Chinese Mainland, Southeast Asia and other markets.
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AlphaPai, an AI-powered investment research platform developed by financial technology company RabyteTech, presented tools for financial research, including gathering data, monitoring financial instruments and comparing information from corporate earnings calls.
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At the event, the team explained how users can give research instructions in everyday language, while the software retrieves and processes information to prepare reports and presentation materials. Examples included tracking market developments and comparing company statements over time. The team also emphasised the importance of traceable sources, allowing analysts to verify the information behind their research.
A2B2.AI, a fintech startup, presented AI- powered tools designed to support investors and wealth advisers. Its specialist AI agents—software designed to carry out particular tasks—help prepare client meeting briefings, monitor financial products approaching maturity and identify market developments relevant to individual clients, according to the team.
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For investors, the tool can help make sense of lengthy documents provided by advisers and bring information from different accounts together to assess risk exposure. For wealth professionals, the tools support preparation and follow-up around client meetings.
The team described its approach as combining AI with human expertise to support financial decision- making. It also clarified that the platform does not execute investment transactions on behalf of clients, leaving those decisions and actions in their hands.
Preferences AI, an AI startup specialising in synthetic consumer research, presented simulated consumer profiles that businesses can use to explore how different audiences might respond to products, pricing and marketing messages. These profiles generate modelled responses rather than feedback from people directly interviewed or surveyed.
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The company described that its profiles are grounded in population data and account for differences between local audiences Businesses can use them to explore pricing or packaging options, as well as ask follow-up questions to understand the reasons behind a particular response. The team also discussed testing its models and working with academics to help validate the approach. It said the technology could help businesses explore consumer preferences more quickly.
ByteFuture, an AI infrastructure startup, focused on running AI on users’ own hardware. It introduced Olares, its operating system, and Olares One, a device designed to run AI models and applications locally.
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Its applications included personal assistance, financial research and content creation. The team argued that users handling private documents or proprietary trading strategies may prefer to retain that material on equipment they control.
A demonstration of AI-generated video illustrated creative work the team said could run locally. It also described applications and agents available through its software marketplace.
PureGlobal, a fintech company, addressed digital asset custody: the systems used to safeguard access to digital assets. Its team described technology for protecting access credentials and supporting offline transactions without a conventional internet connection.
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The presentation focused on infrastructure for institutions holding and transferring digital assets, including security under different operating conditions.
SlowMist, a blockchain security firm, examined the risks of giving AI agents permission to read information and perform tasks. Its examples included malicious instructions concealed in material an agent might encounter and attacks exploiting access granted to software.
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The team discussed security auditing, monitoring and investigations alongside risks arising from third-party tools and an agent’s stored information. One scenario involved exposing a wallet’s recovery phrase, which can give someone control over its assets.