Equipment Financing for AI and Machine Learning Companies

GPU clusters generate heat at a rate that forces hard choices about cooling infrastructure before the first training run starts. A dense AI compute deployment pushing 30 to 80 kilowatts per rack cannot run on perimeter air cooling designed for 5 to 8 kilowatts per rack. The cooling system has to be specced and funded alongside the compute, and the power infrastructure has to match both. AI and machine learning companies that skip this conversation when they are budgeting for GPUs find themselves with expensive compute that cannot run at rated capacity because the thermal environment cannot support it.

We finance the complete infrastructure layer for AI compute deployments: liquid cooling systems, immersion cooling systems, rear-door heat exchangers, UPS systems sized for dense GPU load, and the power infrastructure that delivers clean, reliable power to the compute environment. The GPU is the tip of the capital requirement, not the entirety of it.

A properly spec'd AI compute environment requires infrastructure investment that often runs 30 to 50 percent of the GPU cost or more, depending on the thermal density of the deployment. The physical infrastructure layers we finance:

• Liquid cooling: liquid cooling systems including direct-to-chip cooling manifolds, rear-door heat exchangers, and full immersion cooling tanks for the highest-density deployments. GPU clusters at 30+ kW per rack cannot be air-cooled at any economical scale.
• UPS systems: three-phase UPS scaled to the GPU cluster's power draw. A 100-rack GPU deployment at 30 kW per rack requires 3 megawatts of UPS capacity. Modular UPS systems allow capacity to be added as the cluster grows.
• Standby power: generators sized to sustain the compute environment through a utility outage. Interrupted training runs represent lost time and potentially lost progress on long runs, so the standby power investment is justified by the compute cost it protects.
• Power distribution: power distribution units and busway systems rated for high-density rack power delivery. Standard PDUs are often inadequate for GPU rack power requirements.
• Thermal management: chilled water plants and cooling towers when the AI deployment is large enough to justify a dedicated mechanical plant rather than sharing facility cooling.

The AI compute financing category includes a wide range of company types at different stages of development:

• AI startups building dedicated training infrastructure: companies that have determined it is more cost-effective to own GPU compute than to rent cloud capacity for their training workloads. The break-even point between owned and rented compute has driven many AI companies to build owned clusters.
• Enterprise AI teams: corporate AI and data science teams within large enterprises who are deploying on-premises GPU clusters for proprietary model training and inference. Enterprise teams often have capital budgets and internal approval processes that equipment financing fits cleanly.
• AI inference service providers: companies that run inference workloads for enterprise customers and need reliable, owned compute capacity rather than depending on cloud spot instances that can be interrupted.
• Research institutions: universities and research organizations building AI compute infrastructure for academic research programs. These entities often have grant funding and procurement processes that align well with equipment financing structures.

The most common request from AI companies is to finance the infrastructure alongside the GPU hardware in a single transaction. We accommodate this. Compute hardware and supporting infrastructure can be bundled into a single financing facility rather than requiring separate applications for the GPUs and the cooling and power systems that support them.

Structures we use for AI infrastructure projects:

• Equipment loans for companies who want to own the infrastructure outright and take the full depreciation benefit. Terms of 36 to 60 months are typical for compute and infrastructure.
• Equipment leasing for companies who expect to refresh GPU hardware on a two-to-four-year cycle. A lease structure keeps the payment lower and allows an upgrade path at end of term without being stuck with fully depreciated but technologically obsolete hardware.
• Sale-leaseback for companies who have already purchased infrastructure with venture capital or operating cash and want to recover that capital for operations or additional compute.

For infrastructure packages under $400,000, the application process is streamlined. Above that, we add financials. AI startups with venture funding rounds documented in public filings or investor letters can often use that documentation as part of the financial package.

AI compute infrastructure is concentrated in markets with available power and fiber connectivity. Santa Clara, CA and the broader Silicon Valley region remain primary AI infrastructure markets. Austin, TX has attracted significant AI company infrastructure as the Texas tech sector grows. Seattle, WA is home to significant AI research and enterprise AI infrastructure tied to the major cloud providers headquartered there.

Secondary markets are emerging as power constraints in primary markets push AI compute buildouts to locations with available power. Reno, NV and Phoenix, AZ are both seeing AI infrastructure investment driven by power availability and favorable economics. We finance infrastructure in all of these markets and expect the geographic footprint of AI compute buildouts to continue expanding.

AI and machine learning companies ask questions that reflect both the startup funding context and the specific technical requirements of high-density compute infrastructure.

Send us your compute and infrastructure equipment list. We will structure financing that covers the full deployment, not just the GPU layer. Most approved transactions fund within one to two weeks of a complete application.

Submit your project or call to talk through the infrastructure requirements.

Can a venture-backed AI startup with limited operating history qualify for equipment financing?

Yes. Venture-backed companies with documented funding rounds can use that financial context as part of the credit package. The startup's runway, the round documentation, and the business plan all contribute to the underwriting conversation. Approval may require a personal guaranty from founders or a structure that accounts for the early-stage risk, but access to capital is realistic.

We want to finance GPU hardware and liquid cooling infrastructure together. Can both go in the same transaction?

Yes. GPU compute hardware and the cooling, power, and physical infrastructure that supports it can all be included in a single financing facility. We document each asset category within the facility, but you submit one application and receive one approval.

We purchased our first GPU cluster with venture capital. Can we do a sale-leaseback on that equipment now to recover cash?

Yes. If the equipment was recently purchased and has a clear value, a sale-leaseback converts that equity into cash. The equipment stays in place, serving your training workloads as before. You make scheduled payments to the lender and retain the right to continue using the equipment through the lease term.

How do you handle financing for equipment that may be technologically obsolete in three years?

We take technology obsolescence into account when structuring the term. GPU hardware typically finances on 24-to-36-month terms given the rapid pace of generational change. Infrastructure equipment like cooling and power systems that remains relevant for 10+ years can finance on much longer terms. A mixed facility separates these asset categories with terms appropriate to each.

Can we finance equipment for a bare-metal AI inference service that we will offer to enterprise customers?

Yes. Inference infrastructure that generates revenue from enterprise customers is a financeable asset. The customer contracts supporting the revenue are useful context for underwriting and can accelerate the approval process.