title: Direct Current Data Centers
author: Cjhandmer
content_type: article
publication: Casey Handmer's blog
published: 2026-01-30T00:00:00
source_url: https://caseyhandmer.wordpress.com/2026/01/30/direct-current-data-centers/
word_count: 3048
Casey Handmer, Matt Weickert
Originally posted at the Terraform Blog.
This post explains our current views on how humanity will achieve Kardashev Level 1 status by exploiting the full energy resources of an entire planet. More specifically, how pure solar+batteries will power AI scaleup beyond gas turbine manufacturing limits.
It is an extension to my earlier post of March 2024 on using solar to power AI datacenters, and a response of sorts to the Scale Microgrids paper that showed a mix of solar and gas could reduce emissions for the developers and operators of next gen AI datacenters. In that paper, Kyle Baranko, Duncan Campbell and co-authors showed that around 90% solar with local natural gas backup generators would be the fastest way to get power. In this work, we show that taking this trend to its obvious conclusion and deleting all the legacy fuel-based power components can be even faster and cheaper. We also include a discussion of space-based inference.
Let’s examine this problem from first principles. What is silicon cognition?
You can call it a tensor core, a Blackwell, a GPU, but these are all versions of the same thing. A sliver of silicon with billions of transistors, through which cascade a torrent of electrons converting the entropy of a few volts to the entropy of information generation, and the entropy of waste heat. A GPU is a very complicated switch that regulates current flow, with some other side effects.
For the foreseeable future, the GPU will be the expensive part, currently valued at around $50,000/kW. All it needs to continue to operate is an infinite supply of moderately spicy electrons, that is, a DC power supply at a few volts. Given that making power is much simpler than thinking, the job of the power supply is to be uncomplicated and relatively cheap. In no universe should providing power be the hard part.
Solar and batteries are a natural match to this demand. A solar panel is a slice of silicon (without logic gates) that absorbs solar photons and drives electrons uphill. To a good approximation, a solar module is a constant current source that maxes out at about 40 V. A battery is a reversible chemical reaction that stores and releases electrons, and to a good approximation is a constant voltage source. Modern lithium chemistries hold at about 3.9 V across nearly their entire state-of-charge range.
For logistical reasons related to the relative scarcity of copper in the crust of the Earth, it makes sense to operate solar cells, batteries, and GPUs in series so that the entire system runs at about 1000 V and each electron can be reused a few hundred times.
Our radical claim is that, in the limit, Earth-based AI compute will look like this:
By area, thousands of acres of solar panels.
By cost, a pile of GPUs.
In the limit, Earth-based AI compute will be a direct current (DC) solar array connected to a DC battery bank connected to a DC GPU rack.
This approach brings numerous other advantages:
No grid connection.
No moving parts.
No turbines.
No gas connection.
No nuclear fuel.
No emissions.
No power conversion.
No transformers.
No inverters.
No power transmission.
None of these parts make the AI smarter, and all of them can potentially intrude onto the critical path.
Delete.
It sounds nice in theory, but how can this work?
The key metric to optimize is tokens per dollar. For example, take Scale Microgrids’ work on a 90-10 AI system, increase the size of the solar and battery farm enough to get to 99+% uptime, delete the gas power side, and compare overall economic productivity. A gas system that’s used only 1% or 0.1% of the time still costs time and money, and that’s the core reason why deleting it can end up reducing overall cost.
The graph below shows the tokens per dollar landscape for two hypothetical solar powered AI systems, one with a gas powerplant (blue) and one without (orange). Both have a solar array (size given in nameplate multiples of the peak AI load) and battery size (given in hours of capacity at full load).
The key insight is that there are two stable attractors. One with a pure gas energy supply, with solar and battery supplementation for vibes, CO2 reduction, or marginal capacity expansion. The other with pure solar and batteries, no gas. The pure gas system capex is minimized with no solar and batteries, as natural gas itself is relatively cheap given no preference for emissions reduction. But the two manifolds intersect along a frontier, and beyond that the solar array and battery are capable enough that it’s actually cheaper to delete the gas powerplant entirely.
This tradeoff does not come at zero cost. In exchange for deleting the cost, complexity, and schedule risk of a gas powerplant comes the sizable land demands of a solar array. To a rough approximation, 15 acres of solar are required per MW of DC AI load. For reference, the USA has about 150 million acres of unpopulated desert west of the Mississippi, enough for 10 TW of AI development. 10 TW is much more than total global electricity generation today. There is plenty.
On the other hand, while fracked gas is relatively abundant (for now) the turbines that convert it into power are hard to make, hard to ramp, and largely already spoken for. If AI seeks growth beyond the production ramp of turbines, it is clear which way the wind is blowing.
Before we get to the methods section, I’ll give a rough heuristic for performance. Assuming an on-off binary state on the load, a 15 MW solar + 15 MWh battery can get to ~99% utilization anywhere in the US south west, but is that good enough? The short answer is yes – maximizing tokens per dollar spent, or ROI, justifies throttling demand on a few of the longest, coldest nights of the year.
But it’s actually better than that. Remember that a GPU is a glorified silicon switch intermediating the flow of electrons downhill. Power consumption is proportional to clock frequency multiplied by the square of the voltage (P ~ f V2). GPU power consumption is not fundamental: Token production rate is. If we’ve deleted DC-DC converters then voltage is set by the state of the battery, and frequency is controlled by software. This means that a 3% reduction in token production rate can buy us a 9% reduction in power consumption. So the math changes from 99% utilization to more like 99.7%. This shifts the economics around solar and battery plant sizing considerably, given that GPU frequency modulation allows for a 3x discount in actual utilization and token production.
There is one other implication of these wildly capable and versatile solar+battery AI data centers. They have enough power to operate at full, or nearly full, capacity for the entire year. For 10 months of the year they are oversupplied, and can provide electricity and low grade heat (from their cooling systems) to neighboring customers essentially for the marginal cost of power transport. These could be seasonal or intermittently friendly loads such as the synthetic hydrocarbons and primary materials being pioneered at Terraform, and/or local communities. At Terraform, we believe that power should be as cheap as possible.
Methods
Epistemology. How is it possible for this lightly evolved monkey to *know *these things?
You will need:
A year (at least) of real time solar data from a target location. This is data for an EW fixed tilt array in Texas that we generated by feeding fixed south tilt data into a slightly non-trivial geometry model.
A solar PV module IV curve model. This is based on the JAM72S30-540/MR/1500V but they’re all pretty similar.
A Li-ion discharge voltage curve.
A frequency-power curve for a typical GPU.
Plug these all together in a model that charges the battery when the sun is up, provided the panel voltage is high enough. We initially simulated a system with no power electronics whatsoever, but found that battery charging efficiency was inhibited when the battery state of charge was low, because pulling the panels to a lower voltage actually decreased their efficiency. Given that MPPTs are not that expensive, we could put them back in.
Then, provided the battery can deliver power, the GPUs are powered and we count how many tokens are generated.
Throw in a basic “governor” that throttles the GPU when it predicts the battery will be exhausted before dawn.
This graph shows performance over a ten day period in winter. Note how the governor throttles output early on the fourth day by rationing power until the following morning. The cubic power consumption of GPUs means that throttling a little bit early is much better for token production than running full blast into a wall and then dropping to zero production until the sun comes back up.
Now run thousands of simulations for every combination of battery size and array size, measuring overall utilization of the load.
This chart shows yearly utilization of a GPU asset given solar and battery sizes, including our basic governor. Note that a “steepest” ascent starting at zero solar and zero batteries turns first on adequate solar, then adequate batteries, then marginal solar, then marginal batteries. This reflects the shape of the resource curve and the degree of exploitation required to get to the marginal nth 9 of reliability.
This chart shows curtailment reduction with adoption of the minimum viable governor vs some naive on/off operator, showing 2.3-2.6x improvement, which is close to the 3x implied by the GPU’s cubic power consumption. This governor is not very sophisticated, for example, it has no ability to take weather prediction into account. It merely assesses the time of day, the state of the battery and of solar generation and curtails GPU utilization accordingly.
Throwing in assumptions about capex, we can assess capital efficiency.
This chart shows token production per dollar (in arbitrary units), showing a rather broad peak with considerable flexibility. Adding too much solar or batteries degrades capital efficiency – the correct response is to add more GPUs in this case.
Because the peak is so broad, there is freedom to choose for one additional preference. That is, we can alter the size of the array and the battery by 20-30% with respect to the load and still get much the same return on capital. Given that land is finite, we may want to maximize tokens per acre while holding development cost constant, which puts us towards the lower edge of the peak in the diagram above. Then, holding land use and tokens per dollar equal, adding more battery towards the bottom right of the peak increases absolute token production on a fixed GPU and solar array asset base. This mirrors actual operational optimization, which is to say, pave all available land with solar, then add GPUs and batteries until revenue peaks.
At last, the machinery to perform a comparison with a gas or gas-solar hybrid system is in place. Plug in some assumptions around GPU cost, solar cost, battery cost, gas turbine cost, gas fuel cost, and amortization period, and you can produce this chart.
Here we assume that GPUs are $50,000/kW, batteries (including all ancillary power electronics) are $200/kWh, solar is $200/kW, gas turbines are $2500/kW, gas is $55/MWh, and we’re amortizing over 10 years.
The chart suggests the possibility that under some set of assumptions, it’s actually cheaper to delete the gas power system entirely, so what are those assumptions? For any given cost, select the peak utility point for solar array and battery size and marginalize across these parameters.
The left side of this chart shows where a pure solar system is the best value. As a rough rule of thumb, this is where 1.3 x battery cost + solar cost K = 1
Stellar power: 1026 W -> K = 2
Galactic power: 1036 W -> K = 3
One step on the Kardashev scale is equivalent to increasing power consumption by a factor of 10 billion.
What is the current level of humanity?
Global electricity production: 3.5 TW -> K = 0.65.
Global fuel consumption: 20 TW -> K = 0.73.
Global planetary surface use for agriculture: 13% -> K = 0.91.
If all of Earth’s land was paved with solar PV at 26% efficiency -> K = 1.01.
If the entire Earth including oceans was paved with solar PV at 26% efficiency -> K = 1.06.
If we fill the unshaded dawn/dusk sun synchronous orbital (SSO) band (800 km to 2500 km) with SpaceX AI satellites, 10^17 W available -> K = 1.04.
All of Earth plus the SSO orbital band -> K = 1.085.
Convert the entire Moon into 2 kg/m^2 solar inference at Earth’s solar orbital radius -> K = 1.91.
Convert Mercury into a Dyson sphere (7 kg/m^2 density) @ 26% PV efficiency -> K = 1.9998.
No way to get to K2 without a slightly more efficient solar panel!
Reader Comments
Rohan Shahsays:: Rohan Shah says: January 31, 2026 at 12:28 am I reading enjoyed the modeling and optimization work you presented here, and am inspired to try my hand at this type of thing. One question I have is with regards to your power architecture. Modern processors run on voltages of less than a Volt to minimize power usage and transistor size. They also have large swings in power consumption on timescales on the order of a MS as processors step through execution ( https://journal.uptimeinstitute.com/electrical-considerations-with-large-ai-compute/ ). Both of these indicate to me that some DC:DC regulation is necessary, possibly right next to the chip, to step down the battery voltage to the appropriate voltage, and be able to ride out transient spikes from the GPU. I would love if you could send me your model and data so I could try to add this detail to it. I don’t think that it would hugely change the results of your model, DC:DC power converters are relatively cheap, but it would be interesting to see what type of power distribution architecture makes the most sense. Like Like Reply
Rohan Shahsays:: I reading enjoyed the modeling and optimization work you presented here, and am inspired to try my hand at this type of thing. One question I have is with regards to your power architecture. Modern processors run on voltages of less than a Volt to minimize power usage and transistor size. They also have large swings in power consumption on timescales on the order of a MS as processors step through execution ( https://journal.uptimeinstitute.com/electrical-considerations-with-large-ai-compute/ ). Both of these indicate to me that some DC:DC regulation is necessary, possibly right next to the chip, to step down the battery voltage to the appropriate voltage, and be able to ride out transient spikes from the GPU. I would love if you could send me your model and data so I could try to add this detail to it. I don’t think that it would hugely change the results of your model, DC:DC power converters are relatively cheap, but it would be interesting to see what type of power distribution architecture makes the most sense. Like Like