MiTAC Computing Bets the Future on Turnkey AI Infrastructure

As general manager of MiTAC Computing, Raymond Huang is building the case that validated, pre-integrated infrastructure is the best path to AI at scale. His company, long known as a manufacturer of high-performance compute systems, has evolved into a provider of end-to-end AI infrastructure solutions. In a recent TechArena Data Insights episode, Solidigm’s Jeniece Wnorowski and I talked to Raymond about the strategy, the partnerships, and the technical decisions driving that evolution.

The Evolution to Delivering Turnkey AI Capacity

MiTAC’s transformation has been driven by three converging pressures: the technical demands of modern AI workloads, economic pressure on margins and differentiation, and a customer base that increasingly wants faster time to deployment. In response, the company is developing turnkey AI infrastructure solutions built around pre-integrated rack-level systems in which servers, GPUs, networking, power distribution, and cooling are validated together before they ever reach a customer site.

“The AI factory is a new category we're working on,” Raymond said. “MiTAC is aligned around the modular AI cluster as a building block. We don’t just build servers; we deliver AI capacity.”

That positioning matters because the traditional model, in which customers sourced components separately and spent months integrating them, is increasingly untenable at the pace AI demands. Raymond noted that MiTAC’s approach compresses that timeline significantly, moving customers from months of integration to days or weeks of installation.

Solving the Density, Power, and Cooling Equation

AI clusters are constrained by how tightly GPUs can be packed without hitting thermal or power ceilings. MiTAC addresses this through pre-designed high-density rack configurations validated for specific GPU setups, ranging from 32 air-cooled GPUs per rack to 96 GPUs per rack with liquid cooling. This eliminates the trial-and-error phase that often accompanies high-density deployments.

Power delivery is equally central to the approach. Modern GPU racks can draw easily from 30 kilowatts up to 130 kilowatts, and instability in that power supply creates downstream problems that are expensive to diagnose and fix. Because MiTAC controls both the design and manufacturing of its systems, it can pre-match power profiles to specific GPU and CPU configurations from the ground up. That end-to-end ownership allows the company to engineer across voltage options including 208V, 415V, 480V, and potentially the upcoming 800V DC standard.

On cooling, the company has developed direct liquid cooling systems capable of removing up to 95 percent of generated heat via a liquid loop. Raymond pointed to MiTAC’s G4826Z5, a 4U system built around dual AMD EPYC 9005 CPUs and AMD Instinct MI355X GPUs, as a demonstration of what liquid cooling makes possible.

“Without the DLC, this kind of density wouldn’t even be thermally practical,” Raymond said. “The system isn’t performance throttling even during long AI training runs. So the system sustains peak GPU utilization instead of cycling down due to heat. This is critical to the large language model training or HPC simulation multi-AI workload jobs.”

A Partner Ecosystem Built Around the Full Stack

In our Data Insights series, we often discuss how as AI infrastructure becomes more data intensive, application performance relies on compute and storage working together optimally. Raymond described a set of partnerships that MiTAC has developed to address the layers of the AI stack to enable high performance. For example, with Solidigm and DDN, MiTAC has built a storage solution pairing its servers with Solidigm NVMe drives and DDN software to eliminate the I/O bottlenecks that can leave expensive GPU clusters sitting idle waiting for data.

For orchestration, MiTAC has partnered with Rafay, whose managed Kubernetes platform and GPU orchestration tools simplify cluster management across multi-node environments. The combination allows customers to go from power-on to a usable cluster significantly faster, with centralized policy management that scales consistently from a single rack to 500.

Another collaboration reflects MiTAC’s commitment to reducing the energy footprint of AI infrastructure: its work with Akash Systems using diamond-based cooling. Because diamond conducts heat at roughly five times the rate of copper, the resulting systems consistently run up to 10 degrees Celsius cooler than standard configurations, delivering measurably better performance per watt without increasing energy consumption.

Manufacturing Localization and the Road Ahead

MiTAC is also expanding its North American manufacturing footprint to meet demand for localized supply chains. Raymond described the company’s capacity as several thousand racks per month, with a focus on building, fulfilling, and supporting AI infrastructure locally in each region it serves.

Looking a few years out, Raymond sees data center design converging around liquid cooling as the universal standard, rack densities climbing toward 100 kilowatts to potentially one megawatt per rack, and hybrid on-site power generation becoming common. The organizing principle Raymond returned to is modular, repeatable architecture.

“Think of it like Lego blocks for AI capacity,” he said. “This is extremely critical for deploy to speed needs.”

The TechArena Take

MiTAC’s evolution to an AI infrastructure provider reflects a broader maturation in how the market thinks about deploying AI at scale. Validated, pre-integrated solutions that compress deployment timelines and reduce operational risk are becoming increasingly crucial. Raymond’s message to technology decision makers is straightforward: the competitive question is how quickly and reliably you can put your chosen infrastructure to work. MiTAC understands that customers who face that pressure will value solutions that arrive ready to run.