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There is no universal “cheaper” option. Compare the same GPU count and memory, interconnect, CPU and RAM, storage, region, network transfer, support, software, commitment term and actual utilization. The hourly prices below come from provider pages accessed October 20, 2026; availability and rates change by region and date.
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What the two models actually are
GPU cloud instances
A GPU cloud instance is a virtual machine or managed bare-metal slice rented by time. You select a GPU type, region, operating system and attached services, then pay an hourly or otherwise metered rate. Capacity can be released when a job finishes. Google Cloud documents three relevant purchasing modes:
- On-demand: pay as you go, with full lifecycle flexibility for development, testing and variable production workloads.
- Flex-start VMs: fixed-duration jobs at discounted rates when you can schedule work and accept the associated start-time constraints.
- Reservations: capacity held for immediate or future use, including clustered deployments that need several GPUs together.
Other providers offer comparable constructs under different names. Spot or preemptible capacity can reduce the rate further, but an interruption can stop a training run or require checkpoint and restart logic.
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Rented dedicated GPU servers
A dedicated server is one complete physical machine leased to one customer. You receive the machine’s GPUs, host CPU and memory, local storage lanes and network interfaces rather than sharing those resources with unrelated tenants. A current Velyrix description includes every GPU, CPU core and NVMe lane, plus root access and IPMI/Redfish baseboard-management access, with terms from one to 36 months.
Dedicated rental does not mean the provider operates your software stack. Unless the contract says otherwise, your team normally installs the operating system, NVIDIA or AMD drivers, CUDA or ROCm, containers, schedulers, monitoring, patching and recovery procedures. The provider supplies the physical host, power, connectivity and hardware support.
Current price evidence—and why it is not an apples-to-apples comparison
The following figures illustrate the spread of published prices, not a benchmark or a universal ranking. Cloud prices are generally GPU-hour or node-hour rates; dedicated prices are monthly leases. GPU generation, memory, host configuration, region and commitment terms differ.
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| Offering and source | Published rate | What the figure represents |
|---|---|---|
| Google Cloud NVIDIA T4 | $0.35 per GPU-hour | On-demand rate on the current pricing page |
| Google Cloud NVIDIA P4 | $0.60 per GPU-hour | On-demand rate on the current pricing page |
| Google Cloud NVIDIA V100 | $2.48 per GPU-hour | On-demand rate on the current pricing page |
| CoreWeave eight-GPU HGX B200 | $68.80 per hour on demand; $34.87 spot | Node-hour prices on the current pricing page |
| CoreWeave eight-GPU RTX PRO 6000 Blackwell high-memory node | $20.00 per hour on demand; $11.09 spot | Node-hour prices on the current pricing page |
| Velyrix eight-GPU H100 SXM dedicated server | $24,000 per month; $21,400 per month annual equivalent | Whole-machine lease; annual-equivalent rate requires the stated annual commitment |
| Velyrix eight-GPU H200 SXM dedicated server | $31,100 per month; $27,700 per month annual equivalent | Whole-machine lease; annual-equivalent rate requires the stated annual commitment |
| Azure ND96isr H200 v5 example | $114.65 per hour on demand | Lenovo Press 2026 pricing example |
| AWS p6-b200.48xlarge example | $114.27 per hour on demand | Lenovo Press 2026 pricing example |
| GCP g4-standard-96 example | $14.97 per hour on demand | Lenovo Press 2026 pricing example |
At 730 hours in a nominal month, $24,000 is about $32.88 per hour and $21,400 is about $29.32 per hour; those conversions are arithmetic, not additional provider quotes. A dedicated H100 may therefore look expensive beside a low-end single-GPU cloud rate but competitive with an eight-GPU high-end node once you compare the same GPU count and continuous use.
Lenovo Press modeled approximately 9,800 hours as the break-even for an eight-H200 on-premises system versus a three-year reserved-cloud comparison. In a separate model, an eight-B200 on-premises system became cheaper than AWS on demand at about 5.3 hours per day. Both figures include that publication’s assumptions for hardware, power, cooling, colocation and utilization; they are vendor-published models, not guarantees for your facility or a rented-server contract.
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Normalize the total delivered cost
Use a worksheet that converts every option to the same period and workload output. Start with the cost of one successful training run or one million inference requests, not just the advertised GPU rate.
- Match accelerator capacity. Record GPU model, count, VRAM, memory bandwidth, partitioning and whether the cloud instance delivers the full device. An eight-GPU H100 SXM system is not equivalent to a single T4 or to a different H100 form factor.
- Match the host. Capture CPU sockets and cores, system RAM, local NVMe capacity and performance, boot volume, operating-system licensing and any attached storage charges.
- Match interconnects. Note NVLink or NVSwitch topology, PCIe generation and lane layout, and InfiniBand or other high-speed networking. Distributed training can lose more time to communication than to arithmetic when the fabric is insufficient.
- Add metered services. Include persistent disks, snapshots, object-storage requests, load balancers, orchestration, monitoring, support plans and public-IP charges.
- Price data movement. Include ingress and egress, cross-zone traffic, replication and the cost of moving checkpoints or datasets between your storage and the accelerator.
- Apply utilization. Divide committed monthly cost by hours the GPUs are actually processing useful work. Include queue time, data loading, debugging, idle replicas and maintenance. A server billed for 730 hours but productive for 300 hours has a very different effective rate.
- Include people and migration. Estimate engineering time for image builds, driver upgrades, scheduler integration, security controls, benchmark validation and moving data or checkpoints between providers.
- Model failure and interruption. Spot savings may be erased by lost work without frequent checkpoints. For dedicated hardware, estimate the business cost of a failed host and the provider’s replacement SLA.
For a simple monthly comparison, calculate:
effective hourly cost = (monthly lease + recurring services + expected support and failure cost) ÷ productive GPU hours
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For cloud, replace the lease with instance hours, storage, transfer and service charges. Then calculate cost per training step, tokens generated or requests served so that different hardware can be compared by delivered output rather than nominal time.
How the decision changes with utilization and commitment
Low or uncertain utilization
On-demand cloud avoids paying for idle hardware and lets a small team start immediately. It is usually the safest choice while a model architecture, batch size or customer demand is still changing. Flex-start or spot capacity can lower the price for short jobs if your scheduler can wait and your code checkpoints reliably.
High, steady utilization
If GPUs will process work most of the day for months, a dedicated lease or a reserved cloud commitment can spread fixed cost over many productive hours. The threshold is workload-specific: compare your measured utilization with the provider’s term, cancellation rules and the cost of excess capacity during slow periods.
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Rapidly changing capacity
Cloud APIs can add or release instances in minutes, subject to regional capacity. A dedicated server gives a known amount of hardware; adding another host may require a new reservation, deployment window and network configuration. Teams with unpredictable launches, evaluation bursts or seasonal demand generally value cloud elasticity more than a lower steady-state rate.
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GPU model names do not predict end-to-end performance by themselves. Check the exact memory size, interconnect and topology. An eight-GPU training job may require NVLink or NVSwitch inside the host and InfiniBand between hosts. A cloud cluster reservation can provide that fabric, but only on qualifying instance families and placements. A dedicated server can expose the complete topology and avoid contention from other tenants, while a poorly configured host can still underperform.
- Single-GPU inference: prioritize VRAM, latency consistency, local storage and network path to your application.
- Multi-GPU fine-tuning: verify peer-to-peer bandwidth, collective-communication support and container drivers before committing.
- Distributed pretraining: measure scaling efficiency across the intended node count; scheduler and fabric integration matter as much as GPU count.
- Data-heavy pipelines: check sustained NVMe throughput, dataset locality and whether object-storage reads become the bottleneck.
No controlled cross-provider benchmark establishes that cloud or dedicated hardware is universally faster. Performance depends on the selected instance or server, placement, software versions, workload and contention.
Operations and control
What cloud removes
The provider handles physical host procurement, power, cooling, rack networking and much of the capacity lifecycle. APIs and images speed up reproducible environments, and managed identity, object storage, registries, logs and orchestration can reduce integration work. You still own driver compatibility, container images, data pipelines, quotas, cost controls and application reliability.
What dedicated rental gives you
Root access lets you tune the kernel, drivers, filesystems, containers and schedulers without a virtual-machine abstraction. IPMI or Redfish access can help with remote diagnostics and power control. Single tenancy removes noisy-neighbor risk and makes performance easier to characterize. The trade-off is operational responsibility: you must patch, monitor, secure and recover the host, and coordinate hardware replacement with the provider.
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Failure handling
Ask cloud providers how quickly a failed instance can be recreated and whether local disks survive. For dedicated rental, get replacement-part and whole-server replacement targets in writing. Keep checkpoints in durable storage separate from the machine, automate image rebuilds and test restoring a job on another host or cloud account.
Security, compliance and data sovereignty
A single-tenant node can simplify threat modeling for sensitive inference, but “dedicated” is not itself a certification. Verify the provider’s audit reports, encryption options, staff access controls, logging, destruction process and incident-notification terms. Confirm the physical region and availability zone in the contract if data must remain in a jurisdiction.
CoreWeave describes dedicated inference for organizations needing single-tenant GPU nodes, availability-zone placement for data sovereignty and predictable SLA targets for customer-facing AI systems. Treat that as a product capability to verify against your own contract and compliance framework, not as a blanket compliance claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Workload-by-workload guidance
Development and prototyping
Start with on-demand cloud GPUs. Engineers can create and delete environments, test several GPU generations and avoid a long commitment while requirements are unsettled. Set quotas, automatic shutdown and budget alerts so forgotten notebooks do not become a monthly surprise.
Short, fixed training jobs
Evaluate flex-start or spot capacity when a job has a known duration and can tolerate queueing or interruption. Use frequent checkpoints, deterministic environment images and a restart policy. Keep on-demand capacity as a fallback for deadlines.
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Large distributed training
Compare cluster reservations and dedicated multi-GPU servers on fabric, topology, scheduler support and data locality. Obtain a scaling test at the intended node count before signing a long term. A cheaper GPU with weak interconnects can cost more per completed training run.
Steady customer-facing inference
A dedicated node or dedicated inference service can make latency, tenancy and capacity easier to predict. Size for peak concurrency, then decide whether a smaller baseline plus cloud overflow is cheaper than reserving for the absolute peak.
Compliance-sensitive inference
Favor the option that can document tenant isolation, region placement, access controls and retention. Capture those requirements in the service agreement and validate them operationally with logs and access reviews.
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Keep an always-on baseline on dedicated capacity or a committed cloud reservation, and burst training or traffic spikes to on-demand or spot instances. Route jobs through a common container image and scheduler so workloads can move without rebuilding the stack.
A practical evaluation process
- Measure two to four weeks of demand. Record GPU busy time, memory use, queue length, request latency, training throughput and peak concurrency.
- Define non-negotiables. Write down minimum VRAM, interconnect, region, isolation, recovery time, compliance controls and maximum acceptable interruption.
- Build three cost cases. Model low, expected and peak utilization for on-demand cloud, committed or flex capacity, and dedicated rental. Include all normalized costs.
- Run a representative pilot. Use production-like data and the same precision, batch size, sequence length, checkpoint cadence and networking pattern. Measure cost per token, step or request.
- Stress failure paths. Terminate an instance, simulate a preemption and restore a dedicated host from a checkpoint. Record recovery time and operator effort.
- Negotiate and verify terms. Confirm minimum lease, cancellation, bandwidth limits, support response, hardware replacement, data deletion and renewal pricing.
- Set a review trigger. Recalculate when utilization, GPU generation, region pricing or workload mix changes materially. A monthly utilization report is usually sufficient for a stable service; bursty teams should review after each major project.
Common mistakes and fixes
- Comparing a GPU-hour with a whole node: convert both to the same GPU count and productive hours.
- Ignoring idle time: enable autoscaling, suspend development machines and place nonurgent jobs in a queue.
- Buying for peak demand: use a dedicated baseline with cloud burst capacity unless peak utilization is frequent and predictable.
- Choosing by GPU name alone: inspect VRAM, host RAM, storage, topology and network fabric.
- Treating spot as guaranteed: checkpoint, retry and maintain an on-demand fallback.
- Underestimating operations: budget engineering time for drivers, security updates, monitoring and hardware incidents on dedicated hosts.
- Assuming dedicated means compliant: verify certifications, physical location, tenant boundaries and contractual controls.
- Locking into one provider’s image: package workloads in portable containers, keep data in transferable formats and automate infrastructure definitions.
Decision checklist
Choose GPU cloud first when most answers are “yes”:
- Demand is intermittent, seasonal or not yet measurable.
- You need capacity this week rather than after a provisioning window.
- You benefit from managed storage, identity, orchestration and monitoring.
- You can checkpoint jobs or tolerate spot interruption.
- You want to test several GPU generations before committing.
Choose a dedicated rented server first when most answers are “yes”:
- GPUs will be busy for a predictable fraction of every month.
- Single-tenant isolation, root access or fixed physical placement is mandatory.
- Stable latency and throughput matter more than instant scale-out.
- Your team can operate drivers, containers, schedulers, security and recovery.
- The normalized lease cost is lower after storage, transfer, support and idle time.
Use a hybrid when you have a steady inference baseline plus occasional training peaks, or when compliance requires a dedicated serving tier but experimentation benefits from cloud elasticity.
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