ZincFive’s Nickel-Zinc Answer to AI Power Spikes

As a proud media sponsor of the upcoming AI Infra Summit, TechArena is spending time this summer with companies up and down the AI stack to learn the latest on AI Infrastructure Requirements.

I had the pleasure of sitting down with Brandon Smith, ZincFive vice president of global sales and product, to discuss the rising power requirements for AI factories and the difference between traditional high performance computing (HPC) and AI workloads. Here's what we learned.

Q1: AI workloads don’t behave like traditional data center loads. They spike, collapse, and surge again in milliseconds. How has AI changed the shape of power demand inside the data center, and why do UPS architectures built for grid outages fall short of that new reality?

A: For decades we sized UPS systems around a single event: the loss of grid power. Battery selection, capacity planning, the whole architecture assumed a steady load broken only by a rare outage. AI ended that assumption. An AI workload can swing from idle to full draw and back in milliseconds, then repeat it thousands of times an hour. We call it AI Dynamic Power, and it has become the dominant profile inside modern data centers, not an edge case. A UPS designed to ride out a five-minute outage was never meant to absorb that kind of repeated, high-speed step load. Operators feel it as stress on the system and as headroom they paid for and cannot fully use. The demand did not just get bigger. It changed shape, and the power system has to change shape with it. The name of the game today is actually flexibility. Being able to adapt to current loads but also have flexibility in design to allow future expansion and support of next-gen GPUs and workloads, which are coming faster than ever.

Q2: The default response to that volatility has been to overbuild. Why is overbuilding a losing strategy as power density climbs, and what does it mean to move a UPS from passive backup to active stabilization?

A: The reflex has been to add battery, add headroom, stack another layer to soak up the volatility. This worked when GPU workloads were new and we were learning, but now that these systems are deployed and these workloads are better understood, it's time to optimize. Our industry research shows 84% of operators now rank total cost of ownership at the top of their priorities, and 57% need more power in a smaller footprint. Overbuilding pushes against both. Every redundant layer is capital, floor space, and cooling you pay for to cover a worst case a smarter system could handle on its own. The shift is functional. Power moves from passive backup to active stabilization. Rather than sitting idle until the grid fails, the system absorbs the spikes and releases energy as the workload calls for it, shaping load in real time before it travels through the facility and out to the grid. At that point the UPS is no longer only protecting the site. It is optimizing it.

Q3: You argue the advantage isn’t only architectural, it’s chemical. What makes nickel-zinc suited to the repeated, high-intensity pulses of AI in a way lead-acid and lithium-ion aren’t, and how does the BC2 AI put that to work?

A: The architecture matters, and the chemistry is what makes it possible. Nickel-zinc (NiZn) battery technology is built for high-power, rapid-response work. It takes repeated, high-intensity cycling without the fast degradation other chemistries show under that stress, which is exactly the pattern an AI load creates. Lead-acid is too heavy and too slow, and its life drops sharply under hard cycling. Lithium-ion brings a thermal runaway risk that makes you design around fire before you design around performance. Nickel-zinc runs on a non-flammable, water-based electrolyte, so that risk profile changes. We can site the system closer to critical equipment and remove layers of fire suppression and other costs that dramatically reduce total cost of ownership (TCO) and optimize the design for maximum GPU performance. The BC2 AI brings all of this into one cabinet. It intercepts transient load right at the UPS, absorbs the high-speed spikes, and recharges during the quiet intervals, so dynamic and flexible load management and backup runtime live in a single footprint instead of two separate systems, reducing the overbuild, improving TCO, and optimizing the design with a safe and flexible technology.

Q4: As racks push past 100 kW and space grows scarce, footprint and total cost of ownership now matter as much as raw performance. How does a high-power, compact chemistry change the math on space, cooling, and cost for an AI data center?

A: Space inside an AI data center is now a hard constraint. When a rack pulls more than 100 kilowatts, every square foot you give to backup power is a square foot taken from compute. A high-power chemistry changes that math. Nickel-zinc delivers more usable power per square foot, so you get the protection you need without oversizing the room around it. That runs straight into cost. A smaller footprint means less cooling load, simpler installation, and lower operating expense across the life of the system. The safety profile takes out cost too, since you are not wrapping the batteries in elaborate fire suppression. Add a longer service life and it becomes a total cost of ownership story, not only a performance one. The most efficient system is not the one with the most components. It is the one that delivers the performance, safety, and runtime you need with the least infrastructure built around it.

Q5: Beyond the data center walls, unmanaged load swings strain the grid and slow interconnection, already a years-long bottleneck. How does stabilizing demand at the source change a facility's relationship with the grid, and where does power infrastructure have to go next?

A: How a facility behaves at the fence line is starting to matter more than how it behaves inside. Large, unmanaged load swings do not stop at the wall. They reach the grid, and in power-constrained regions that affects stability and how fast a utility will clear new capacity. Interconnection queues already run for years and utilities understand that variable GPU-based workloads are a strain on their system and not a welcome addition to their aging infrastructure. A site that smooths its own demand internally reads as a better neighbor, and that can shape how much capacity gets allocated and how quickly a project breaks ground. That is where power infrastructure has to go next. It stops being a static box sized for a worst case and becomes a dynamic system that shapes demand at the source.