Tag: GPU

I tried to compare the power consumption of GPUs in a way that makes it easier to imagine.
This is not a precise comparison, and since it only looks at power consumption, it may lead to misunderstandings regarding heat generation or efficiency.
Still, to get an intuitive sense of how much energy today’s GPUs consume, this kind of simplification can be useful.

Let’s start with something familiar — a household heater.
A typical ceramic or electric heater consumes about 0.3 kilowatts on low and roughly 1.2 kilowatts on high.
We can use this 1.2 kilowatts as a reference point — “one heater running at full power.”

When you compare household appliances and server hardware in the same units, the scale difference becomes more tangible.
The goal here is to visualize that difference.

Power Consumption (Approximate)

A household heater represents the level of power used by common home heating devices.
A conventional server rack, typical through the 2010s, was designed for air-cooled operation with around 10 kilowatts per rack.
In contrast, modern AI-ready racks are built for liquid or direct cooling and can deliver 20–50 kilowatts per rack.
The NVIDIA H200’s figure reflects the official specification of a current-generation GPU server, while the next-generation GPU is a projection based on industry reports.

Next, let’s convert this into something more relatable — how many heaters’ worth of electricity does a GPU server consume?
This household-based comparison helps make the scale more intuitive.

Heater Equivalent (Assuming One Heater = ~1.2 kW)

Until the 2010s, a standard data center rack typically supplied around 10 kilowatts of power — near the upper limit for air-cooled systems.
However, the rise of AI workloads has changed this landscape.
High-density racks designed for liquid cooling now reach 20–50 kilowatts per rack.
Under this assumption, a single GPU server would nearly fill an entire legacy rack’s capacity, and even in AI-ready racks, only one to three GPU servers could be accommodated.

• NVIDIA H200 (Current Model)

  • Per Chip: up to 0.7 kW
  • Per Server (8 GPUs + NVSwitch): ~10.2 kW
  • Equivalent to about 8.5 household heaters
  • Nearly fills a conventional 10 kW rack
  • Fits roughly 2–4 servers per AI-ready rack

• Next-Generation GPU (Estimated)

  • Per Chip: around 1.0 kW (based on reported estimates)
  • Per Server (8 GPUs + NVSwitch assumed): ~14.3 kW
  • Equivalent to about 12 household heaters
  • Exceeds the capacity of conventional racks
  • Fits roughly 1–3 servers per AI-ready rack

Looking at these comparisons, the difference between a household heater and a GPU server becomes strikingly clear.
A GPU is no longer just an electronic component — it’s effectively part of the power infrastructure itself.

If you imagine running ten household heaters at once, you start to grasp the weight of a single GPU server.
As AI models continue to scale, their power demands are rising exponentially, forcing data center design to evolve around power delivery and cooling systems.
Enhancing computational capability now also means confronting how we handle energy itself, as the evolution of GPUs continues to blur the line between information technology and the energy industry.

In the past, the foundation of the economy was gold.
Under the gold standard, currency was backed by physical assets.
The rarity of gold itself directly reflected the credibility of a nation and the value of its currency. That was the world we once lived in.

After a long phase of economic expansion unmoored from tangible assets, we are now entering a world where computational capacity is beginning to take the place once held by gold.

AI has become the foundation of all economic activity.
Industries are run by models. Decisions are made by computation.
In such a society, value is no longer created by labor—but by computational resources themselves.

And what are computational resources?
They are, in the physical sense, electricity, compute devices, cooling infrastructure, access as a matter of policy, and above all, semiconductors.

In the coming world, a nation’s credibility will be determined by how much it can compute.
National power will increasingly reflect the total computational capacity it controls.
A country that possesses semiconductor design and fabrication capabilities—and the energy and infrastructure to operate them—will be able to anchor its currency with computational resources.

This represents a transition into a compute-backed economic system.

Where once nations signaled their monetary credibility with gold reserves, they may soon point to the total number of GPGPUs they own, the strength of their AI training infrastructure, or the volume of high-quality data they control.
We may enter a world where it is reasonable to say, “Our currency is stable because we possess sufficient compute.”
It’s possible that compute has already rewritten the very concept of military power.

Computational resources are invisible. And their value is fluid.
Electricity prices, cooling efficiency, software optimization, algorithmic efficiency, and data quality—all of these dynamically affect the credibility of a currency.
This is a real-time economic foundation, so dynamic that humans alone may be unable to grasp it.
It presupposes communication between AIs.
And for any nation without computational resources to participate in that communication, the end may already be near.

Until now, the economy has been driven by “intangible trust.”
But in the age of AI, it is “the total executable compute” that becomes the final form of trust.
And at the core of that trust lies the hard fact of how much compute a nation possesses—and governs—within its borders.

Semiconductors, electricity, and data are no longer merely parts of industrial structure.
They underpin currency and sovereignty.
And the nation that supports them will be the one that holds the next global reserve currency.