The race to Superintelligence is officially underway. Large language models (LLMs) and multimodal AI tools have already transformed how we work, interact and build. Inference AI systems are now powering real-time applications across industries. Today’s most advanced AI systems, despite their impressive capabilities in reasoning and creativity, represent only a fraction of what artificial intelligence will ultimately achieve. Leading organizations such as OpenAI, Anthropic, Meta, Google and emerging labs like SSI (Safe SuperIntelligence Inc.) are now focused on something even more ambitious: Superintelligence—an AI agent that surpasses the brightest human minds in reasoning, learning and creative problem-solving across both training real-time inference labs.

Just a few weeks ago, Meta announced the formation of TBD Labs and acquired top talent from Scale AI, signaling the urgency and magnitude of this pursuit. Yet while headlines spotlight the models, breakthroughs, and algorithms, the backbone of this transformation is often overlooked: infrastructure.

According to IDC, spending on AI-centric systems is expected to reach $632 billion by 2028. In this race to Superintelligence, the real challenge is building gigawatt-scale infrastructure—massive, purpose-built data centers designed to support unprecedented levels of computation and orchestration.

A New Class of AI Infrastructure 

Traditional data centers weren’t designed for AI’s unprecedented demands–the gigawatt-scale power, extreme cooling needs and ultra-low latency interconnection required to support next-generation AI models. Inference AI workloads, in particular, demand millisecond response times for production applications. As AI continues to grow exponentially in complexity, these traditional systems are being stretched beyond their limits. According to NVIDIA’s CEO Jensen Huang, next-gen AI models will require “100 times more compute than older models.” These models must process vast amounts of structured, unstructured, synthetic and real-world data to power everything from robotic perception to real-time language generation and high-throughput inference processing.

Supporting this evolution requires:

• Massive parallelism across tens of thousands of GPUs
• Ultra-low latency networking for inter-node communication
• Energy-optimized clusters capable of 44kW+ per rack
• Scalable cooling technologies, including liquid-ready designs
• Secure, high-throughput interconnection across hybrid environments

This marks a turning point: AI infrastructure must now operate as intelligence-scale systems—modular, adaptive and built to handle compute intensity far beyond general-purpose cloud workloads.

Columbus: The First Superintelligence Cloud Deployment 

As demand for high-performance compute grows, Columbus, Ohio is emerging as a critical hub for digital innovation. With demand spanning industries like healthcare, finance, manufacturing and logistics, Columbus offers ideal proximity for enterprises seeking low-latency infrastructure. It’s well-positioned to meet both local and national demands for AI-powered solutions. A real-world example is unfolding in Columbus, where Lambda and Cologix are building gigawatt-scale AI-ready infrastructure in partnership with Supermicro.

Cologix’s Scalelogix℠  data center is at the core of this project, delivering the high-density power, cooling and low-latency interconnection that advanced AI systems require. With 4,000 GPUs deployed at COL4 by Lambda in under 90 days, this infrastructure supports companies looking to stay ahead in the AI race. Each partner brings expertise at critical layers:

• Cologix provides the foundation with COL4, a data center designed to support high-density, low-latency, interconnected AI workloads.
• Supermicro supplies optimized servers built on NVIDIA’s HGX B200 platform, purpose-built for AI performance and scalability.
• Lambda bridges the technology with full-stack deployment expertise, from hardware orchestration to enabling production-ready AI applications supporting both large-scale training and high-performance inferencing workloads.

While the physical racks, the geography and the design principle may vary from one deployment to another, the core design remains the same: build quickly with trusted partners, scale seamlessly and future-proof the infrastructure to meet the demands of AI innovation.

Another critical dimension of Superintelligence Cloud deployments is the ability to maintain high uptimes throughout scaled training and inference workloads: notoriously, one hyperscaler recently used a 16,384 GPUs cluster and experienced, on average, an unexpected failure roughly every three hours across the system. Lambda, Supermicro and Cologix mitigate these failures with on-site parts depots and technical on-site staff, guaranteeing fast response times and high security standards.

The Time to Build Is Now

The road to Superintelligence will be won not only by those with the best models but by those with the fastest, most flexible and best supported infrastructure to power them. Superintelligence-scale deployments must be futureproofed for the evolving thermal and power envelope of new hardware generations while remaining responsive to changing workloads and customer demands.

Just as factories powered the industrial revolution, inferencing at the gigawatt-scale with AI infrastructure will fuel the next era of global innovation.