Inside Mercuryo’s Visa Partnership
Updated
January 29, 2026 1:34 PM
Close up of Visa credit cards. PHOTO: ADOBE STOCK
Mercuryo is a fintech startup that builds the infrastructure to enable money to move seamlessly between crypto and traditional banking systems. In simple terms, it works on the problem of turning digital assets into usable cash.
As more people hold crypto through wallets and exchanges, one practical issue keeps arising: how do you actually withdraw that money and use it in the real world? For many users, converting tokens into local currency is still slow, confusing or expensive. That gap between “owning” crypto and being able to spend it is where Mercuryo operates.
The company’s latest step forward is a partnership with Visa to improve what is known as “off-ramping” — the process of converting crypto into fiat currency like dollars or euros. Until now, this has often been slow, expensive and confusing for users. Mercuryo is using Visa Direct, Visa’s real-time payments system, to make that process faster and more direct.
With this integration, users can convert their digital tokens into local currency and send the money straight to a Visa debit or credit card. The transaction happens through systems that already power global card payments, which means the money can arrive in near real time instead of days later.
Technically, this connects two very different worlds. On one side is blockchain-based crypto, which moves value on decentralised networks. On the other side is the traditional payment system, which runs on banks, cards and regulated rails. Mercuryo’s platform sits between the two and handles the conversion and movement of funds.
Instead of users leaving their wallet or exchange to cash out, Mercuryo allows the conversion to happen inside the apps and platforms they already use. The user does not need to understand the plumbing behind it. They just see that crypto becomes spendable money on their card.
This matters because access is what makes any financial system usable. If people cannot easily move their money, they treat it as locked or risky. Faster off-ramps make digital assets more practical, not just speculative.
Mercuryo’s work is not about creating new tokens or trading tools. It is about building the pipes that let money move smoothly between Web3 and the traditional financial world. The Visa partnership strengthens those pipes by using a global, trusted payments network that already works at scale.
Visa also framed the partnership as a bridge between systems. Anastasia Serikova, Head of Visa Direct, Europe, said: "By leveraging Visa Direct's capabilities, Mercuryo is not only making converting to fiat faster, simpler and more accessible than ever—it's building bridges between the crypto space and the traditional financial system. This integration empowers users to seamlessly convert digital assets into fiat in near real time, creating a more connected and convenient payment experience".
Over time, this kind of infrastructure is what determines whether crypto remains niche or becomes part of everyday finance. Not through headlines, but through systems that quietly reduce friction.
Mercuryo’s direction is clear: make digital assets easier to use, easier to exit and easier to connect to the money systems people already rely on.
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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.