GPU clusters, purpose-built training facilities, AI-ready data centers, and inference hosting platforms — $10M to $1B+. PeerSense structures the capital stack across project finance, equipment financing, CMBS, and private credit so you can focus on deploying compute.
500+ Capital Sources
Debt, equity, and lease providers
No Retainers
Fee at closing, agreed upfront
Infrastructure-Grade Advisory
Structured for institutional review
From single GPU clusters to billion-dollar campus builds, PeerSense structures capital for the full spectrum of AI compute infrastructure.
Ground-up or retrofit data center construction optimized for large-scale model training. High-density power, liquid cooling, and redundancy built to sustain multi-month training runs on thousands of GPUs.
Capital for GPU-as-a-Service and inference platforms. Whether you are deploying H100/B200 clusters for external customers or building dedicated inference infrastructure, we structure the financing around contracted revenue and utilization projections.
Financing for colocation facilities purpose-designed for AI workloads — high power density per rack, advanced cooling, and connectivity to major cloud on-ramps. Existing colo operators expanding into AI-ready capacity or greenfield builds.
Distributed compute facilities positioned at the network edge for low-latency inference. Smaller footprint, high power density, and proximity to end users. Ideal for autonomous vehicle networks, content delivery, and real-time AI applications.
There is no single product for AI infrastructure. The right capital stack combines multiple instruments based on asset type, stage, counterparty credit, and offtake structure.
Non-recourse or limited-recourse debt structured against projected cash flows. SPV-based structures for large-scale builds with long-term offtake agreements, power purchase contracts, or contracted compute revenue.
Typical: 60–80% LTC, 7–20yr tenor
GPU clusters, networking hardware, cooling systems, and power infrastructure financed through equipment loans or operating/capital leases. Preserves balance sheet flexibility while deploying compute at scale.
Typical: 3–7yr term, up to 100% of equipment cost
For stabilized data center assets with in-place tenancy and predictable cash flows. Non-recourse, fixed-rate permanent financing competitive with any institutional alternative.
Typical: 60–75% LTV, 5–10yr fixed
Flexible capital from private credit funds, infrastructure debt funds, and institutional investors. Bridge financing, preferred equity, and subordinated debt to fill gaps in the capital stack that senior lenders will not cover.
Typical: $10M–$500M+, bespoke structures
Monetize existing data center assets or GPU hardware through sale-leaseback transactions. Extract capital from deployed infrastructure while maintaining operational control and long-term use.
Typical: 80–100% of FMV, 10–20yr lease
Short-term capital for ground-up builds, facility retrofits, and pre-stabilization periods. Interest-only structures that bridge to permanent takeout once the facility reaches target utilization.
Typical: 12–36mo, interest-only
AI infrastructure is fundamentally a power and cooling problem. Lenders and equity investors underwrite the physical plant as rigorously as the revenue model.
AI training and inference workloads require 40–100+ kW per rack — 5 to 10x traditional enterprise compute. Capital structures must account for utility interconnection costs, on-site generation, and power redundancy at scale.
Liquid cooling (direct-to-chip and immersion) is now standard for high-density GPU deployments. Lenders evaluate cooling infrastructure as a critical underwriting factor — inadequate cooling means stranded compute capacity.
Securing reliable multi-megawatt power at competitive rates is the single largest constraint on new AI infrastructure builds. PeerSense works with developers who have grid interconnection agreements, PPAs, or on-site generation strategies.
Training runs spanning weeks or months require N+1 or 2N redundancy on power and cooling. Capital providers evaluate facility design against SLA commitments and the cost of interrupted training cycles.
AI-driven demand is reshaping commercial real estate financing at a scale not seen since the telecom buildout of the late 1990s. Data center construction, GPU procurement, and power infrastructure represent the largest single category of new institutional lending in 2026.
Goldman Sachs projects global data center power demand will increase 165% by 2030, reaching approximately 200 gigawatts — effectively doubling today's installed base. By 2027 alone, AI-driven workloads are expected to account for 27% of total data center demand, with inference workloads overtaking training as the dominant AI compute requirement. The remaining demand splits between cloud services (50%) and traditional enterprise workloads (23%).
JLL's 2026 Global Data Center Outlook describes this as an "AI supercycle" — a structural transformation in how capital is deployed into physical infrastructure. Current vacancy sits at a record-low 2.3%, with 8 GW of colocation capacity under construction. Critically, 73% of capacity under construction is already pre-leased, indicating that demand continues to outpace supply even at historically high construction rates.
This supply-demand imbalance is driving rent growth of approximately 12% CAGR over the trailing 3–5 years, with global pricing reaching $217.30 per kilowatt per month — up 3.3% year-over-year. For lenders, these fundamentals make data center assets among the most financeable commercial real estate in the market today.
The data center financing market is dominated by large transactions. The average data center loan is $1.2 billion, though the median is $40 million — reflecting a market with a few hyperscale mega-deals and a large number of mid-market colocation and enterprise builds. Asset-backed securities (ABS) volume for data centers grew 40% year-over-year to $7.7 billion in H1 2025, while single-asset single-borrower (SASB) CMBS issuance surged from $1.7 billion to $5.7 billion. Total CMBS issuance for data centers hit an all-time high of approximately $4.5 billion in Q1 2025 alone.
Capital is available for data center projects at a level that sets them apart from nearly every other commercial real estate asset class. While office, retail, and even some multifamily segments face tightening credit conditions, data center lending is expanding. The underwriting thesis is simple: contracted demand from investment-grade hyperscalers (Microsoft, Google, Amazon, Meta, Oracle) provides cash flow certainty that few other asset classes can match.
Power availability — not land, not capital, not construction capacity — has emerged as the dominant constraint shaping where, when, and how data centers can be developed. New data center projects in power-constrained markets face 3–5 year utility interconnection timelines. This has shifted development activity to markets with available power capacity (central Ohio, Texas ERCOT zones, parts of the Southeast) and created a premium for sites with existing utility infrastructure.
Power Purchase Agreements (PPAs) have become a critical financing component. Physical PPAs provide direct renewable power from a generation asset to the data center, offering price certainty for 10–20 years. Virtual PPAs function as financial hedges against market electricity prices. In either case, a signed PPA with a creditworthy counterparty significantly improves the financeability of a data center project — lenders view it as contracted cost certainty comparable to a long-term lease.
For operators developing behind-the-meter power solutions — on-site natural gas generation, fuel cells, or nuclear microreactors — the power infrastructure itself becomes a separately financeable asset through project finance structures, often at 60–80% loan-to-cost ratios.
CoreWeave's financing trajectory illustrates both the opportunity and the risk in GPU-backed lending. The company has accumulated over $11 billion in total borrowings, primarily through delayed-draw term loan (DDTL) facilities collateralized by GPU hardware and customer contracts. DDTL 1.0 (2023, up to $2.3 billion) carries floating rates around 15%. DDTL 2.0 ($7.6 billion) averages approximately 11%. In early 2026, NVIDIA injected an additional $2 billion in equity, and CoreWeave sought $8.5 billion in new financing using Meta infrastructure contracts as collateral.
The challenge: GPU market rental rates have fallen 50–70% from peak, shrinking collateral value just as CoreWeave faces a $4.2 billion principal repayment in 2026. This "GPU debt wall" is a cautionary tale for any operator financing AI compute infrastructure — hardware depreciates rapidly, and lenders price this through shorter tenors (3–5 years), higher rates (10–15%), and residual value haircuts.
The lesson for PeerSense clients: GPU-backed debt works when collateralized by long-term, contracted revenue from creditworthy customers — not speculative utilization. The strongest GPU financing structures pair equipment-level debt with facility-level project finance, separating the rapidly-depreciating hardware from the slower-depreciating real estate.
While the CHIPS Act's Advanced Manufacturing Investment Tax Credit (25% for semiconductor manufacturing, potentially increasing to 30% under pending Senate legislation) does not directly cover data centers, the Inflation Reduction Act provides significant incentives for data center energy infrastructure. Solar arrays, battery storage, fuel cells, and geothermal systems serving data centers qualify for a minimum 30% investment tax credit (ITC), which can be monetized through tax equity structures or transferred via the IRA's transferability provisions.
Additionally, over 30 states offer data center-specific incentive programs — sales tax exemptions on equipment, property tax abatements, and utility rate concessions. Virginia, Texas, Ohio, and Georgia lead in incentive value. These state programs can reduce the effective all-in cost of a data center development by 10–20%, materially improving project finance returns and lender credit metrics.
No retainers. Referral fee at closing. Initial consultation is complimentary.
Market data sourced from Goldman Sachs Research, JLL 2026 Global Data Center Outlook, CBRE North America Data Center Trends, S&P Global, DataCenter Knowledge, and CoreWeave SEC filings. Rates and terms are indicative and depend on project specifics.
Three steps from initial conversation to term sheet. PeerSense handles capital sourcing, structuring, and negotiation across all layers of the stack.
Share the project scope: facility type, power requirements, GPU deployment timeline, offtake contracts, and target capitalization. A 30-minute conversation is typically enough for us to assess the financing landscape.
PeerSense identifies the right mix of project finance lenders, equipment lessors, private credit funds, and infrastructure investors for your specific deal. We present your project to multiple capital sources simultaneously to drive competitive terms.
Receive term sheets structured to your timeline. We negotiate across capital providers to optimize blended cost of capital, covenants, and flexibility — then coordinate through closing.
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PeerSense works on AI infrastructure transactions from $10M to $1B+. Below $10M, conventional equipment financing or venture debt is typically more efficient. At $10M and above, the deal justifies structured capital solutions — project finance, institutional equipment leasing, or private credit facilities that optimize cost of capital.
We structure capital for purpose-built training facilities, GPU cloud and inference hosting platforms, AI-ready colocation facilities, edge AI deployments, and associated power and cooling infrastructure. The common thread is capital-intensive physical infrastructure supporting AI compute workloads with identifiable revenue streams or offtake commitments.
GPU depreciation is a real underwriting consideration. Lenders mitigate this through shorter lease tenors (3-5 years), residual value insurance, contracted revenue against the hardware, and the borrower's ability to refresh hardware at maturity. Facilities with diversified GPU generations and long-term customer contracts receive more favorable terms.
For project finance structures, contracted revenue significantly improves terms — long-term compute agreements, reserved capacity contracts, or anchor tenant leases. For equipment financing and private credit, lenders will consider a combination of contracted and projected revenue, but the more committed the cash flow, the more competitive the capital.
Data center construction debt typically finances hard and soft costs through a 3–5 year term loan at 60–80% loan-to-cost. The shorter tenor avoids technology obsolescence risk. Lenders underwrite based on tenant creditworthiness (hyperscaler offtake agreements), pre-leasing levels, power availability, and the developer's track record. Interest is typically capitalized during construction. Once the facility stabilizes with signed leases, the construction loan is refinanced into permanent debt (CMBS, ABS, or life company) at significantly lower rates.
Data center lending is the most active category in commercial real estate as of 2026. Asset-backed securities volume for data centers grew 40% year-over-year to $7.7 billion in H1 2025. CMBS issuance for data centers hit $4.5 billion in Q1 2025 alone — an all-time record. Total U.S. data center lending exceeds $121 billion. Vacancy is at a record 2.3% with 73% of capacity under construction already pre-leased. Capital is more available for data centers than any other CRE asset class.
Power availability is the primary constraint on data center development — not capital or land. New projects in power-constrained markets face 3–5 year utility interconnection timelines. Lenders view secured power access as a critical underwriting factor. A signed Power Purchase Agreement (PPA) with a creditworthy utility or renewable provider significantly improves financeability. Projects with behind-the-meter power solutions (on-site generation, fuel cells, microreactors) can finance the power infrastructure separately through project finance at 60–80% LTC.
The Inflation Reduction Act provides a minimum 30% investment tax credit for renewable energy systems serving data centers — solar, battery storage, fuel cells, and geothermal. Over 30 states offer data center-specific incentives including sales tax exemptions on equipment, property tax abatements, and utility rate concessions. Virginia, Texas, Ohio, and Georgia lead in incentive value. These programs can reduce all-in development costs by 10–20%.
PeerSense charges no retainers and no consulting fees for AI infrastructure advisory. Our compensation is established upfront before any work begins and is paid at closing. The initial consultation is complimentary.
Whether it is a $10M GPU cluster or a $1B training campus, PeerSense structures the capital. One conversation. Competitive term sheets from lenders who understand AI infrastructure.