The SMR Solution: Why Big Tech is Betting on Small Modular Reactors to Power the AI Revolution

AI workloads have changed the math for energy. Modern large language models and foundation models scale compute far faster than available grid capacity in many regions. The result: Big Tech is looking beyond traditional power procurement — toward small modular reactors (SMRs). This post explains what SMRs bring to the table, how companies can integrate them with datacenter design, and a practical sizing example engineers can reuse when evaluating deployments.

Why power is the bottleneck for AI

AI training and inference at scale are power-hungry and predictable. A single training pod with thousands of GPUs can draw megawatts continuously for weeks. Unlike consumer web workloads, these loads:

• Are high density — many megawatts in a few buildings.
• Are continuous — multi-week runs with tight uptime requirements.
• Demand low-latency power — interruptions cost compute hours and model regressions.

The electrical grid in many markets was never designed for these concentrated, persistent loads. New transmission lines cost years and billions of dollars, and local utilities often cannot guarantee capacity on the timelines cloud providers need.

What SMRs offer: a practical summary

SMRs are a class of nuclear reactors designed to be smaller, modular, and factory-built compared to traditional gigawatt plants. For cloud and AI infrastructure they provide:

• Predictable baseload power at 20 to 300+ megawatts per module — enough to run dense clusters.
• High capacity factor — consistent energy availability year-round.
• Reduced need for grid upgrades — colocating an SMR with a datacenter reduces dependency on long transmission buildouts.
• Footprint efficiency — smaller land and site prep compared with equivalent renewables-plus-storage for continuous supply.
• Long operational life and fuel logistics that scale differently from fossil or renewable assets.

SMRs aren’t a silver bullet: siting, licensing, and community acceptance matter. But for companies that need guaranteed, compact, and low-carbon supply, SMRs are attractive.

Deployment models: how Big Tech is thinking about SMRs

There are three practical models engineers should consider.

1) Co-located SMR + datacenter

A company finances or partners on an SMR adjacent to its datacenter campus. Benefits:

• Direct wired connection; reduced transmission losses.
• Easier capacity planning and redundancy design.

Challenges:

• Site permitting and community engagement.
• O&M and lifecycle responsibilities.

2) Dedicated offsite SMR with long-term offtake

An SMR developer builds offsite; the operator buys a long-term power purchase or capacity contract. Benefits:

• Avoids on-site nuclear footprint.
• Offsets carbon while guaranteeing supply.

Challenges:

• Still requires transmission; reduced benefit versus co-location.

3) Hybrid SMR + renewables + storage

SMR provides baseload and quick ramping is handled by batteries or gas peakers for short-term demand swings. This is a practical system design for accommodating both continuous training and burst inference.

Technical considerations for engineers

When assessing an SMR solution, focus on these technical vectors:

• Power density per rack: plan for peak power per rack rather than average. Use P_peak = racks * P_per_rack.
• Cooling architecture: many SMRs interface easily with water-cooling loops; design for closed-loop heat rejection and consider reuse options (district heating or absorption chillers).
• Electrical configuration: direct connect with step-up transformers, redundant switchgear, and on-site UPS sized for rapid transient coverage.
• Load-following: while many SMRs are optimized for baseload, some advanced designs support limited ramping. Complement with batteries for fast transients.
• Safety and security integration: physical perimeter, radiation monitoring, cybersecurity for plant control systems.

Cost and timeline trade-offs

• Capital intensity: SMRs have high upfront capital but low marginal energy cost and long asset life.
• Levelized cost of energy (LCOE): SMRs can be competitive versus gas and renewables-plus-storage for continuous high-load consumers because they remove the need for massive battery fleets or overbuilt renewables.
• Lead times: licensing and construction can add years. Early movers can lock favorable offtake pricing, but teams must plan around multi-year procurement cycles.

A practical sizing example

Below is a simple Python-style calculation engineers can use to estimate SMR capacity for a training campus. Use this model as a starting point for sensitivity analysis.

# Inputs
racks = 500                   # number of GPU racks
power_per_rack_kW = 30        # peak power per rack in kW
pue = 1.2                     # power usage effectiveness
redundancy_factor = 1.2       # spare capacity for redundancy

# Compute peak facility demand in kW
peak_it_kW = racks * power_per_rack_kW
peak_facility_kW = peak_it_kW * pue * redundancy_factor

# Convert to MW and add margin
peak_facility_MW = peak_facility_kW / 1000.0
capacity_margin = 1.15
required_smr_capacity_MW = peak_facility_MW * capacity_margin

print("Peak IT (kW):", peak_it_kW)
print("Peak facility (MW):", round(peak_facility_MW, 2))
print("Required SMR capacity (MW):", round(required_smr_capacity_MW, 2))

Interpretation: 500 racks at 30 kW each with a PUE of 1.2 and 20% redundancy yields a peak facility demand. Multiplying by a 15% margin yields the SMR nameplate you’d target. Swap racks and power_per_rack_kW for your cluster.

Integration patterns: electrical and cooling

• Electrical: configure the SMR output to feed an on-site substation with N+1 redundancy in switchgear. Use modular transformers and synchronous condensers if voltage stability is a concern.
• Cooling: match the plant’s heat rejection temperature to your datacenter chillers. Consider direct heat reuse for absorption chillers to improve overall site efficiency.
• Controls: build a clear interface between plant control systems and datacenter energy management. Avoid exposing plant controls to public networks; insist on air-gapped or strictly segmented communications.

Regulatory and social factors (engineer’s checklist)

• Licensing milestones: identify key regulatory gating items early.
• Environmental impact: thermal discharge, water use, and emergency planning zones.
• Community engagement: proactive outreach reduces permit risk.
• Decommissioning and fuel handling plans: include lifecycle accounting in the business case.

> SMRs change the procurement and risk model. Engineers must translate that change into electrical single-line diagrams, cooling integration, and lifecycle operational plans.

Summary checklist — what to evaluate now

• Power profile: measure peak, sustained, and transient loads for target clusters.
• Sizing model: run the Python-style example with real racks and power_per_rack_kW numbers.
• Site options: compare co-located vs offsite SMR based on transmission cost and lead time.
• Cooling match: validate heat rejection temperature and potential for heat reuse.
• Redundancy design: plan N+1 electrical and UPS layers; include battery buffers for transients.
• Regulatory timeline: map licensing milestones into project schedule.
• Community plan: prepare stakeholder engagement documentation and safety briefings.

Conclusion

SMRs are not a replacement for distributed renewables and storage; they are a complementary tool in the energy toolbox. For AI workloads where predictable, high-density, continuous power is a core requirement, SMRs shift the calculus: they lower long-term energy risk and simplify capacity planning. Engineers evaluating SMR solutions should start with hard numbers — peak rack power, PUE, and redundancy — then map those into site-specific electrical and thermal designs. With a clear technical plan and regulatory roadmap, SMRs can be the infrastructure backbone that enables the next wave of AI at scale.

Checklist (one more time): quantify loads, simulate the sizing model, evaluate co-location vs offtake, validate cooling and grid interfaces, and insert regulatory timelines into your project plan.