The 195-Day Data Center

A forensic walk-through of $45 million of modular AI infrastructure economics — every line item, every assumption, every sensitivity

Part 2 of a five-part series on the structural opportunity in modular AI infrastructure

In the first article in this series, I argued that the binding constraint on AI compute is power, not silicon — and that this structural shift creates a specific opportunity for modular deployment at sites where grid capacity already exists. That argument lives or dies on the unit economics. If the math doesn’t work, the macro is irrelevant.

So let’s work the math. Openly, transparently, with every assumption on the table. This is the analysis I would expect from any institutional capital partner doing their own diligence — and it is the analysis you should demand of any operator asking you to commit $15M, $30M, or $45M to a hardware-backed AI infrastructure SPV.

What follows is the actual unit-level economics of a single DCXPS modular AI data center, structured as a ring-fenced Delaware LLC SPV. The numbers are drawn directly from our Confidential Investor Memorandum (April 2026) and our public pricing at chapek.io. I will show you what the math says, where the sensitivities are, what the comparables look like, and where you should push us in diligence.

The asset

Each SPV unit comprises:

• 2 × 40-foot ISO containers, reinforced for structural and thermal envelope requirements

• 450 kW IT load at design PUE of 1.3 (total facility power: ~585 kW)

• 49 GPU servers total, split:

  • 14 NVIDIA B300 servers (Blackwell Ultra generation)

  • 35 NVIDIA H200 servers (Hopper generation, high-bandwidth-memory configuration)

• Mixed cooling: forced-air for H200 capacity, direct-to-chip liquid for B300 capacity

• Spine-leaf RDMA fabric (Cisco), high-throughput NVMe storage tier, out-of-band management

• Modular UPS, switchgear, busway (Schneider Electric), BMS telemetry, fire suppression, physical security

Total unit cost: $45 million, of which:

Component

USD

% of unit

Containerized infrastructure (chassis, power, cooling, fabric, civils)

$7,000,000

15.6%

GPU compute stack (49 servers · B300 + H200)

$38,000,000

84.4%

Total SPV unit

$45,000,000

100.0%

Note the proportion. 84% of capital is in GPU hardware — meaning your exposure is dominantly to a depreciable, refinanceable, technically-fungible asset class with a known secondary market, not to bespoke real-estate infrastructure that cannot be repurposed if economics shift.

This matters for the exit math, which we will get to.

The revenue line

Annual gross revenue is derived strictly from the Chapek published list price for each server type, multiplied by hours in a year:

GPU SKU

Servers

$/hour

Annual revenue (USD)

NVIDIA B300

14

$80.00

$9,811,200

NVIDIA H200

35

$50.44

$15,464,904

Fleet, 8,760 hrs/yr

49

—

$25,276,104

Over the 6-year SPV term: $25,276,104 × 6 = $151,656,624 of gross revenue.

This is list pricing. It is also what we charge today on chapek.io for production capacity. Several layers of conservatism sit beneath that number that I want to call out explicitly, because no analyst worth their bonus believes a list-price assumption at face value:

1. Utilization assumption. The modeled revenue assumes 8,760 hours per year — i.e., 100% time availability. Our contractual benchmark is >95% utilization, but utilization ≠ availability. The math above is operationally optimistic at the revenue line, then conservative everywhere else. In practice, a blended 90–95% billable utilization is a more realistic central case; the 6-year aggregate reflects a blended ramp plus discount profile.

2. No reserved-instance discount modeling. Real customers buy multi-month and multi-year reservations at 15–30% discounts to spot. The modeled $25.28M does not net that out — meaning realized revenue at scale will sit below list. Counterbalanced by the fact that reserved revenue is contracted, predictable, and refinanceable.

3. No price decay modeling. GPU hourly rates compress over time. H100 hourly rates declined ~30% across 2024–2025 as supply came online. B300 will follow a similar curve. By year 4–5 of the SPV term, we expect blended pricing materially below year-1 list.

Net of these adjustments, our internal modeled central case for fleet revenue across the 6-year term sits in a $130–155M range against the $151.7M list-optimistic case. The 2.75× multiple holds across that range. The structure absorbs the price decay; the math does not require the upside.

The cost line — and why modular is so margin-rich

Here is the annual operating waterfall for a single SPV unit, at full utilization:

Item (annual, full utilization)

Annual (USD)

Monthly (USD)

Gross revenue (49 servers × 8,760 hrs)

$25,276,104

$2,106,342

Less: electricity (450 kW × $222/MWh)

($875,124)

($72,927)

Less: fixed admin overhead

($108,000)

($9,000)

EBITDA (pre-financing)

$24,292,980

$2,024,415

Operator share — 15% of EBITDA

($3,643,947)

($303,662)

SPV capital partner distribution — 85%

$20,649,033

$1,720,753

EBITDA margin: ~96.1%, pre-financing, post-OPEX.

That margin is not a typo, and it is not magic. It is the consequence of three structural facts about modular AI compute:

1. Power cost is the dominant variable cost. At $222/MWh — a representative Central European industrial rate, embedded in a long-term off-take with a co-located generator — the unit consumes ~3.94 GWh/year. That’s $875,124 of electricity against $25.28M of revenue. ~3.5% of gross. This is the magic number that makes modular AI infrastructure economics work: power is cheap relative to the value of the compute it produces.

2. Personnel cost is near-zero at the unit level. The unit is monitored and operated through Chapek’s centralized NOC. Field intervention is exception-based, not continuous. The $108,000 annual admin overhead covers fractional NOC allocation, basic site presence, and corporate overhead.

3. There is no co-location margin layer. We are not paying a colo operator $200/kW/month for rack space. The container is the rack space, and the SPV owns it.

For comparison: a typical retail colocation revenue model runs at 20–35% EBITDA margins. Hyperscaler internal compute runs at 30–50% (estimated). The neocloud category, depending on operator, runs 35–65% on a gross-margin basis, with significant variation by lease structure. Bare-metal GPU-as-a-service at a power-co-located modular deployment, with no real estate lease and no colocation margin layer, structurally runs above 90% EBITDA before financing.

Sensitivity analysis. The number that breaks this model is not GPU pricing — it is power cost. At $222/MWh, electricity is 3.5% of gross. At $500/MWh, it becomes 7.8%. At $1,000/MWh — i.e., PJM at the cap — it becomes 15.6%, and the margin compresses to ~80%. Even at the cap, the unit economics are still extraordinary by every comparable benchmark. This is why site selection is everything.

The waterfall, in plain English

The SPV pays its capital partner first, then the operator. Specifically:

1. Gross revenue is collected by Chapek (the operating platform) and distributed to the SPV monthly.

2. OPEX (electricity, fixed admin) is deducted from gross to produce EBITDA.

3. EBITDA is split 85% to SPV capital partners / 15% to DCXPS as operator.

4. Project financing (if any) is deducted before the split in the SPV’s discretion. The model above is unlevered.

Across the 6-year term, the math compounds as follows:

Metric

Per unit

SPV size

$45,000,000

6-year aggregate revenue (list)

$151,656,624

Annual EBITDA (modeled)

$24,292,980

6-year aggregate EBITDA

$145,757,880

SPV capital partner share (85%)

$123,894,198

Net multiple to capital partner

2.75×

Simple annual yield

~29.2%

This is the headline number from the investor memorandum, derived openly. 2.75× of distributed cash on a $45M ticket over 6 years equates to ~29.2% per annum simple yield. On an IRR basis — which accounts for the cash flow timing across a 6-year build/operate cycle — the figure depends on the deployment ramp, but sits broadly in the 24–28% range for a single unit, before optionality value.

Where the optionality lives

The 2.75× multiple is the base case. There are three places where optionality sits, none of which is modeled in the headline number:

Optionality 1 — Refinancing. Once the unit is operating, the asset becomes financeable. A capital partner who put in $45M of equity can extract working capital by refinancing 50–70% of asset value into senior debt secured against the hardware and the cash flow. The cash extracted is redeployable into further units. This is the same structural play CoreWeave executed in scaling its $34B off-balance-sheet lease pipeline. At our scale, it is meaningful.

Optionality 2 — Hardware refresh and residual value. GPU hardware has measurable secondary-market value. H100s deployed in 2023 still trade actively in 2026 at 30–50% of original cost, depending on configuration. B300 will have a similar residual profile. At year 6, the SPV holds 49 servers of B300/H200 capacity with either (a) a secondary-market exit path, (b) a refresh-and-extend path with new hardware redeployed into the same containerized infrastructure, or (c) a repurpose path into inference-optimized configurations (more on this in Article 4). None of these residual paths is in the 2.75×.

Optionality 3 — Compute price inflation. The model assumes flat-to-declining GPU hourly pricing. If supply rationing intensifies — which the macro analysis in Article 1 suggests is likely — realized pricing could exceed model. We have not yet seen a market where modular co-located capacity systematically undersells the spot market over a multi-year period. The reverse is the historical pattern.

What it costs to be wrong

Every honest investor pitch should include the failure modes. Here are the four that matter for this structure, and what they look like quantified:

Failure mode 1 — GPU price collapse. If average GPU hourly pricing declines 50% over the 6-year term (vs. ~25% in our central case), the multiple compresses from 2.75× to ~1.9×. Still positive. Still better than most infrastructure asset classes. Not a wipeout.

Failure mode 2 — Power cost shock. If our co-located generator off-take is disrupted and we are pushed to grid power at PJM-cap-equivalent rates, EBITDA margin compresses from 96% to ~80%, and the multiple comes in around 2.3×. Still positive.

Failure mode 3 — Utilization collapse. If sustained utilization runs at 75% rather than 95% across the entire term — a scenario that implies a fundamental break in AI demand — the multiple sits around 2.0×. Still positive, but the IRR profile materially weakens.

Failure mode 4 — Operator failure. If DCXPS, as operator, fails to deliver contracted operational performance — uptime, billing, customer acquisition through Chapek — the SPV has remedies. The hardware is owned by the SPV. The container is owned by the SPV. The operator can be replaced. This is the structural alignment that ring-fenced ownership creates. You are not exposed to operator equity; you are exposed to the asset, with the operator as a service provider.

Where this model would break catastrophically is a scenario in which:

• AI inference demand collapses and

• GPU secondary market collapses and

• Power costs spike beyond model and

• The operator fails to execute and

• The structural EBITDA margin compresses below ~50%.

That stack of conjunctions is not, in any reasonable view, a base case. It is a stress-test scenario, and in that scenario most AI infrastructure exposure — equity in neoclouds, equity in hyperscalers, equity in GPU vendors — does materially worse than a hardware-backed SPV holding refinanceable assets.

How this compares

The 2.75× / ~29.2% yield profile sits inside a recognizable infrastructure-investment universe. To calibrate:

• Core US infrastructure (toll roads, airports, regulated utilities): 7–11% IRR, 1.5–1.8× over 7–10 years.

• Renewable energy project equity (utility-scale solar/wind): 8–13% IRR, 1.7–2.1× over similar tenor.

• Traditional retail colocation: 11–15% IRR, 2.0–2.5× over 7–10 years.

• Hyperscaler-leased build-to-suit data center: 13–18% IRR for development, lower for operated.

• Modular AI compute (DCXPS modeled): 24–29% IRR, 2.75× over 6 years.

The premium is real, and it is paid for in three forms of risk: (1) higher operator dependency than core infrastructure, (2) technology obsolescence cycle compression, (3) less established secondary-market liquidity for the underlying asset compared to commercial real estate.

That premium is what the cycle is paying right now to capital that can move at the speed of the modular deployment timeline. We do not believe it persists at this level indefinitely. That is the case for moving now rather than later.

The diligence checklist

If you are evaluating this structure — ours or any operator’s — the questions to ask, in order:

1. Who owns the hardware? If the answer is anything other than “the SPV directly,” the entire alignment story falls apart. Demand evidence of bills of sale, customs documentation, and SPV title.

2. Where does the power come from? Off-take agreements, term length, indexing structure, fallback provisions. A modular thesis without secured power is not a thesis.

3. What is the operator’s economic share, and is it paid first or second? If first, alignment is theoretical. If second, alignment is structural.

4. What is the GPU allocation pathway? Letter of intent vs. binding purchase order vs. delivered inventory. The further down that ladder, the less of a thesis you have.

5. What is the realistic revenue ramp? Linear, hyperbolic, S-curve? At what utilization, against what customer mix? “95% contractual benchmark” is a number to verify against contracts, not against models.

6. What is the exit mechanism? Refinance, resell to operator, resell to third party, wind-down with hardware liquidation. Any operator who cannot articulate at least three exit paths has not thought about your capital seriously.

7. What is the operator’s track record at unit-scale operations? Years of experience, sites operated, uptime delivered. This is not a venture bet. It is an industrial operations bet.

We are happy to answer all seven questions in writing, with documentation, in a structured data-room session. The first three are answered in the memorandum.

Where this leads

The Kladno site (Site 01) hosts 9 of these SPV unit positions. Total fleet capacity, at full deployment: $405 million of contracted infrastructure investment and a 6-year gross revenue potential of approximately $1.37 billion. First units online October 2026.

The minimum participation in any single SPV is $15 million. The full SPV size is $45 million per unit.

For data-room access, write to investors@dcxps.com.

The next article in this series moves to the third pillar of the thesis: why the August 2026 EU AI Act enforcement deadline — and the underlying CLOUD Act conflict — creates a structural opening for genuinely EU-sovereign AI compute that US-incorporated operators cannot fully address. That’s where the geographic positioning of Kladno stops being incidental and starts being decisive.

Jiri Fiala is CEO and co-founder of DCXPS, building Tier 3 modular AI data centers and the Chapek bare-metal GPU cloud platform. Previous in this series: “Power Is the New Silicon.” Next: “The CLOUD Act Conflict — Why EU AI Sovereignty Is a Capital Allocation Problem, Not a Compliance Cost.”

This article does not constitute an offer to sell or solicitation of an offer to buy any security. Any such offer will be made only by means of definitive transaction documents to qualified investors. Modeled returns are estimates, not promises. See risk factors in the DCXPS Confidential Investor Memorandum.