CUDO Compute

Day 1 at @NVIDIAGTC set the tone for the week ahead. From Jensen Huang's keynote to conversations happening across the conference floor, the scale of ambition around AI infrastructure is unmistakable. The focus is quickly moving beyond GPUs alone to the realities of deploying AI at production scale. As our CBDO & Cofounder, @CudoPete shares from the ground at GTC, many of the discussions are already pointing to the infrastructure challenges behind scaling AI. Challenges that increasingly come down to land, power and compute. If you're here this week, come and meet the CUDO Compute team on the ground to discuss why the next phase of AI deployment is increasingly being shaped by land, power and compute.

Another theme coming up in conversations at GTC: The economics of inference. As models run longer reasoning chains and handle larger contexts, the focus is shifting toward efficiency per watt and throughput per megawatt. Those numbers start to change the economics of AI infrastructure pretty quickly. When efficiency improves at that level, the constraint moves away from silicon performance and toward something much more physical: Power availability. Facility design. Operational environments that can actually support the load. Which brings the conversation back to infrastructure again. #GTC #Inference #AIInfrastructure

AI innovation is moving incredibly fast. But the conversations we’re having at NVIDIA GTC reveal a growing challenge across the industry. Clients are arriving with huge compute requirements. Training runs are larger. Inference workloads are scaling. And organizations are moving quickly to deploy AI into production. The question many teams are now facing is not just access to GPUs. It is how quickly infrastructure can be designed, deployed and operated to support the scale of demand emerging across the market. That’s where many of the discussions on the ground at #GTC are landing this week. Our team is here all week speaking with builders, operators and partners about what it takes to support the next wave of AI deployment.

Everyone’s talking about GPUs. But the numbers are starting to reveal a deeper shift in AI infrastructure economics. The efficiency gains emerging across the stack are staggering. Inference per watt improving by an order of magnitude. Token costs potentially collapsing. Throughput per megawatt accelerating dramatically, with architectures like Rubin paired with Groq 3 LPX pushing inference density to new levels. Individually these metrics are impressive. Together they point to something much bigger. When efficiency scales like this, the constraint shifts. Not models. Not chips. Infrastructure. Land to build on. Power to run it. Compute environments engineered to operate at scale. Which raises the real question for the industry: Who actually has the infrastructure ready to run what’s coming next? Our team is on the ground at #GTC all week discussing exactly this: Matt Hawkins (@HawkinsTech) Pete Hill (@CudoPete) Dean Fletcher Barry Kick Nick Gardener Dennis Hamann Vince Howard

#AI demand is reshaping the entire capacity curve. By 2030, AI workloads are projected to be ~70% of total data center demand, with global capacity projected to nearly triple. Use our framework to pressure-test your planning model: cudocompute.com/blog/ai-data-c…

We are attending @NVIDIAGTC next week in San Jose. #AI factories are moving from concept to real operating environments. @HawkinsTech & @CudoPete will be on-site with the CUDO team to support enterprise 2026 deployments.

#AI campuses don’t scale like traditional data centers. At 100+ MW, grid access, transformer windows, cooling, & sequencing set the pace. If you can’t model time-to-power, you can’t model capacity. Use this as your planning framework before you commit: cudocompute.com/blog/ai-data-c…

If your facility is designed around 50 kW racks while the market moves to 120 kW today and higher next, your “capacity” is theoretical. Design for density jumps inside the facility’s useful life. Explore our insights on turning facilities decisions into token economics: cudocompute.com/blog/ai-data-c…

Data center capacity is becoming the hard limit on #AI scale. Demand is so high that single tenants are now taking entire facilities rather than just racks. @CudoPete has been clear: capacity on paper is no longer capacity in practice.

AI delivery breaks when functions drift out of alignment. Engineering pushes for capacity. Finance pushes for control. Leadership carries the risk. The teams that scale don’t remove this tension. They make the trade-offs explicit and executable.

Transformer lead times moving to 3–4 years forces a new question: which infrastructure decisions are reversible vs locked in? That’s the real risk model for #AI buildouts. Read our full capacity planning framework: cudocompute.com/blog/ai-data-c…

#AI growth won’t be won by ambition alone. As @cudopete points out, the next wave depends on local execution, the right infrastructure, and the right people in the right places. Strategy sets intent. Execution delivers outcomes.

#AI capacity planning is now gated by time-to-power. When activation can take 24–72 months, “add a pod at 70% utilization” stops working. Read our complete framework breakdown: cudocompute.com/blog/ai-data-c…

Our cofounders @HawkinsTech and @CudoPete had an eventful week at @CiscoLive EMEA in Amsterdam. Big takeaway: #AI wins need infrastructure that performs within real power and deployment limits. Thanks to everyone who connected with us and shared industry insights.

Designing #AI factories means integrating power, cooling, layout, and operations so infrastructure fades into the background. That is how compute runs at full potential. Read our guide that covers all of this in-depth: cudocompute.com/blog/designing…

Telemetry closes the loop. #AI training rarely fails loudly. It slows down. Trends in power, temperature, flow, and pressure reveal performance degradation long before alarms fire, provided systems are built to monitor them.

Data centers built a decade ago were never designed for modern #AI workloads. Facilities engineered for 5-10 kW racks are now being pushed to sustained densities of 30-100 kW. This breaks old assumptions fast. Read our thread on infrastructure bottlenecks below.