Tencent's large penguin statue in front of a building. PHOTO: UNSPLASH
Tencent’s latest solution simplifies cross-border payments for Weixin users and merchants.
Updated
November 28, 2025 4:18 PM
Tencent's large penguin statue in front of a building. PHOTO: UNSPLASH
In a world where digital borders are fading faster than ever, Tencent is betting on familiarity. With the launch of TenPay Global Checkout, the company wants to make paying across countries feel as seamless as paying at home.
The new service, unveiled today, allows Weixin Mini Program merchants outside mainland China to accept a variety of local payment methods. That includes digital wallets, real-time payment networks and credit and debit cards, all through a single integration. The launch starts in Singapore and Macao SAR, where merchants can now take payments via PayNow, BOCPAY(MO), and major cards. Japan, Australia and New Zealand are next, with more regions to follow soon.
This rollout builds on the growing reach of Weixin Mini Programs, known internationally through WeChat. These small apps are built right into the platform, letting users' shop, book services and make payments without downloading separate apps. Today, there are over one million monthly active users in key overseas markets, with Mini Programs available across 92 countries and regions.
Yet, for many users abroad, paying within Mini Programs hasn’t always been simple. Foreign card restrictions, currency conversions and limited local options often made checkout a frustrating step. TenPay Global Checkout aims to change that.
“TenPay Global Checkout marks an important step in enhancing the local consumer experience. By enabling overseas Weixin Mini Program merchants to accept trusted and diversified local payment methods through one unified solution, users benefit from a more convenient and efficient payment experience. This helps merchants improve payment conversion rates, expand their user base and scale their businesses to serve a broader range of customers”, said Wenhui Yang, CEO of TenPay Global (Singapore).
What makes this move interesting isn’t just its technical simplicity—it’s the cultural bridge it builds. For users in Singapore or Japan, paying with PayNow or a local card inside Weixin feels less like an international transaction and more like an everyday purchase.
For merchants, it’s an invitation into a market that values convenience and trust. Payment familiarity, after all, often decides whether a user completes a purchase or abandons it at checkout.
The company remains focused on creating secure, connected and user-friendly payment experiences that help merchants grow and allow consumers to pay with confidence, wherever they are.
If successful, TenPay Global Checkout could quietly redefine how cross-border commerce feels—not like a transaction across regions, but a familiar tap, scan or click. In an increasingly global marketplace, that kind of familiarity might just be the next frontier in digital trust.
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The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
December 16, 2025 3:43 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.