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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteNeither colocation nor cloud is universally better for AI computing. Cloud is often the more practical fit for uncertain, bursty, or short-lived workloads; colocation with owned or controlled GPUs merits a full-cost comparison when demand is sustained and the hardware can be kept useful. A hybrid design can make sense when workloads have different utilization, data-location, or latency requirements.
First, compare the right infrastructure models
Cloud and colocation describe different things. Public cloud provides shared computing infrastructure on demand. Colocation is a facility arrangement: you supply or control the IT equipment and use a data center’s space and supporting services, such as power, cooling, and connectivity. The OECD also distinguishes privately owned compute clusters from public cloud and notes that some AI-focused “neocloud” providers offer on-demand AI compute. The OECD’s 2025 report provides useful context for these categories.
That means a quote for a cloud GPU instance is not automatically comparable with a quote for a managed AI service, dedicated cloud capacity, a GPU-focused cloud, or customer-owned servers in a colocation facility. Identify what equipment, operations, and services each option includes before comparing them.
| Decision factor | Cloud compute | Customer-owned equipment in colocation |
|---|---|---|
| Who provides the compute equipment? | The provider supplies on-demand infrastructure; the precise service boundary depends on the product. | The customer supplies or controls the IT equipment. |
| Facility responsibility | The provider operates the data-center facility; the customer still evaluates service fit and capacity. | The facility supplies space and supporting capabilities such as power, cooling, and connectivity; the customer must verify the facility fits the hardware. |
| Scaling and commitment | On-demand access can suit changing or short-term needs; prices, capacity, and commitment terms vary. | Capacity depends on hardware procurement and facility arrangements; assess utilization, financing, refresh, and operating needs. |
| Best initial question | Can the required GPU configuration be obtained in the needed region and time window, at a workable total cost? | Can the organization operate and keep the equipment sufficiently utilized, and can the facility support its power, cooling, and connectivity needs? |
Compare total cost for the workload, not one GPU rate
A cloud hourly rate is only one input. Build a model around the actual workload and include the costs that apply to each architecture:
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- Cloud: compute, commitments or discounts, storage, network transfer, managed services, and any capacity terms.
- Colocation: servers and financing, depreciation, power and cooling, rack space, cross-connects and other connectivity, support, staffing, maintenance, and hardware refresh.
- Both: software, onboarding and exit costs, utilization, and the cost of capacity that sits idle.
Lenovo’s 2025 total-cost study compares selected H100, H200, and L40S server configurations with selected cloud instances. Its stated scope focuses on server acquisition, power, and cooling; it excludes ancillary costs such as managed services, storage, and data transfer. Treat the results as a modeled example under the report’s assumptions, not a universal purchasing rule, and rerun the comparison with current quotes and your utilization estimates. Read Lenovo’s 2025 TCO study.
What Lenovo’s break-even example does—and does not—show
For one ThinkSystem SR675 V3 configuration with eight H100 NVL GPUs, Lenovo Press gives an on-demand cloud instance cost of $98.32 per hour and estimates cloud-versus-owned break-even at approximately 8,556 hours, or 11.9 months of usage. Those are figures from Lenovo’s specific 2025 model, not a live cloud quote or a general threshold: the comparison uses a modeled on-premises system price and power-and-cooling estimate and excludes some ancillary costs. A different configuration, utilization pattern, cloud region, facility quote, or price can produce a different result.
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Cloud pricing is also a moving input. Google Cloud lists GPU prices by region and notes that GPUs are available only in specific zones in some regions. Its pricing page recommends using the pricing calculator with the GPU and machine configuration; Spot prices are dynamic and may change up to once every 30 days. Check current regional availability and terms rather than treating a published rate as a fixed benchmark. Google Cloud GPU pricing.
Model low, expected, and high utilization, as well as deployment delays, hardware refresh timing, and cloud price changes. Compare total monthly spend and cost per completed training run or unit of inference output. A single break-even number can hide the assumptions that matter most.
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Compare end-to-end performance and capacity
Do not choose based on a GPU model name or peak-performance claim alone. Realized training and inference performance depends on accelerator type and memory, inter-GPU and storage networking, data movement, availability, and application latency. The ability to obtain capacity in the required region and time window can matter as much as nominal scale.
Cloud reduces the need to procure and operate the data-center facility, but it does not remove the need to assess instance fit, networking, storage, capacity, pricing, and utilization. Colocation can suit organizations that want to place dense GPU systems in a facility designed for high power and cooling demand, or need connectivity to other networks or cloud services. NVIDIA’s DGX-Ready Colocation program describes certified facilities for AI deployment on NVIDIA DGX and features including interconnectivity and liquid cooling. Its page names operators including Aligned and CoreSite; treat these as providers to investigate, not an endorsement or guarantee of availability in your market. NVIDIA DGX-Ready Colocation Data Centers.
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The sources cited here do not establish a neutral, apples-to-apples benchmark showing that cloud or colocation is inherently faster. When feasible, benchmark representative jobs on the candidate configurations with realistic data paths and target users. Measure throughput, latency, utilization, queue time, and failure and recovery behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for data location, latency, and operational constraints
Data residency, sovereignty, and latency-sensitive edge inference can change which architecture is viable. AWS’s 2025 guide lists these among inference considerations. Lenovo’s comparison notes that on-premises processing can keep data within an organization’s network perimeter, while cloud involves third-party data handling and shared infrastructure. Neither point, by itself, establishes whether a particular deployment meets a legal or security requirement: controls and obligations depend on the provider, service, contract, configuration, and jurisdiction. AWS’s 2025 guide to generative AI infrastructure costs discusses infrastructure planning considerations.
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Also check operational readiness. Owned hardware requires a plan for procurement, deployment, maintenance, support, and refresh; colocation does not make those responsibilities disappear. For either option, establish required uptime, security controls, capacity timing, and who will respond when a job or system fails.
A practical decision process
- Separate the workloads. For each training, fine-tuning, batch-inference, or online-inference job, record accelerator memory and count, expected run hours, utilization pattern, storage and network demand, latency target, and growth uncertainty.
- Set the hard constraints. Specify data location and jurisdiction, security controls, uptime, required capacity date, facility power and cooling needs, and whether your team can operate the hardware.
- Request comparable quotes. Ask cloud providers to itemize compute, commitments, storage, egress, managed services, and capacity terms. Ask colocation providers to itemize space, power, cooling, connectivity, support, and other facility charges; include equipment, financing, staffing, and hardware refresh in your model.
- Calculate a range. Test low, expected, and high utilization, deployment delays, GPU refresh timing, and cloud price changes. Track both total spend and cost per completed job or unit of output.
- Benchmark representative jobs. Where practical, test the actual candidate configurations and realistic data paths; record throughput, latency, utilization, queue time, and failure and recovery behavior.
- Evaluate hybrid placement. Consider putting stable baseline demand on one architecture and variable peaks on another, or placing workloads differently when their latency or data-location requirements diverge.
Which option should you choose?
Start with cloud when you need to move quickly, demand is uncertain or intermittent, or managed access to compute is valuable. Give colocation with owned or controlled GPUs a serious total-cost evaluation when demand is sustained, utilization could justify the hardware, and your organization can handle the associated operational burden. Assess hybrid deployment when a single placement does not fit every workload. The decision should follow workload-specific costs, measured performance, capacity, and constraints—not a universal claim about one infrastructure model.
Quick Recap
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