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The right GPU-cloud purchase is a capacity-and-risk decision, not a search for the lowest hourly rate. First size the workload and required GPU topology; then compare on-demand, spot and reserved capacity using the cost per completed result. Finally, negotiate an outage contract that distinguishes service uptime from the provider actually having GPUs available. For critical training, keep portable checkpoints and a tested second-region or second-provider path.
Start with the workload, not a GPU model
Write down the workload before requesting quotes. A provider can offer the latest accelerator and still be a poor fit if the node layout, memory, storage or network path does not match your job.
Record the sizing inputs
- Model memory: include parameters, optimizer states, gradients, activation memory, framework overhead and any safety margin. Quantization changes the requirement, so record the precision used in training or inference.
- Batch and sequence shape: batch size, sequence length, image resolution or audio duration often determine activation memory more than parameter count.
- Target throughput or latency: specify tokens per second, samples per second, images rendered, or inference response-time objectives.
- Parallelism: document data, tensor, pipeline and expert parallelism. This determines how often GPUs exchange data and how sensitive the job is to interconnect performance.
- Checkpoint interval and restart time: interruption cost depends on how much work can be lost and how quickly a replacement job can resume.
- Storage footprint: include training data, preprocessed shards, checkpoints, container layers, logs and temporary files.
- Data movement: estimate ingress, inter-region traffic, replication and egress. A cheap GPU can become expensive when every checkpoint crosses a region.
Run a small representative benchmark rather than extrapolating from a synthetic GPU score. Measure completed training steps or inference requests, memory headroom, checkpoint duration and scaling efficiency at the intended batch size.
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Match the GPU topology to the communication pattern
Aggregate VRAM is only the first filter. Two eight-GPU systems can behave very differently when a model uses tensor or pipeline parallelism.
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Memory per GPU and total memory
Check both per-GPU VRAM and the usable total after framework reservations. A model that fits only when memory is pooled across GPUs may require fast peer-to-peer links; it cannot be treated as eight independent cards.
GPU count per node and interconnect
Ask for the exact node configuration, GPU-to-GPU interconnect, topology map and bandwidth. High-bandwidth links matter for all-reduce, tensor parallelism and mixture-of-experts traffic. If communication crosses a slower host path, adding GPUs can reduce rather than improve throughput.
Host, storage and network resources
- Confirm host CPU model and core count for data loading, tokenization and preprocessing.
- Confirm system RAM, local NVMe scratch capacity and whether scratch survives a reboot or replacement.
- Measure network bandwidth to object storage and to any database or feature store.
- Check whether the scheduler can place the required number of GPUs on one node or within the same high-speed fabric.
For inference, include request routing, batching and model-replica placement. A single large GPU may minimize latency, while a multi-GPU node may maximize throughput but add synchronization overhead.
How many GPUs do you need?
Use a staged calculation instead of selecting a fashionable SKU.
- Establish the one-GPU baseline. Measure memory use and completed work per unit of time at the production data shape.
- Find the minimum memory-feasible configuration. If one GPU cannot hold the model and required state, test the smallest topology that can.
- Measure scaling efficiency. For each candidate count, record
efficiency = (one-GPU throughput × GPU count) ÷ measured throughput. Include communication and input-pipeline overhead. - Apply the service objective. Select the count that meets the throughput or latency target with operational headroom, not merely the smallest count that passes a benchmark.
- Add failure and maintenance capacity. Production services may need an extra replica or spare node so a maintenance event does not breach latency objectives.
A training run that scales poorly can cost less on fewer GPUs even if its wall-clock duration is longer. Conversely, a launch deadline can justify a larger reservation when the value of finishing sooner exceeds the additional GPU time.
Choose on-demand, spot or reserved capacity
| Commitment | Best fit | Main advantage | Risks and questions |
|---|---|---|---|
| On-demand | Experiments, bursty work and uncertain demand | No long-term obligation; capacity can be released when the project changes | Higher list price and no guarantee that a scarce SKU is available when you need it |
| Spot | Checkpointed jobs that tolerate interruption | Lower price when spare capacity exists | Preemption, queueing and possible loss of progress; verify notice period, restart behavior and whether storage continues billing |
| Reserved or private cluster | Predictable production utilization and guaranteed access | Capacity commitment, stable placement and often dedicated support | Take-or-pay exposure, idle reservations, limited portability and long contract terms |
For every reservation, ask whether the contract is take-or-pay, whether capacity can move between regions or GPU SKUs, and what happens when the named SKU is unavailable. A promise to provide “equivalent” hardware should define acceptable memory, interconnect and performance, not just a similar product name.
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On-demand planning
On-demand is appropriate while the model, utilization or launch schedule is changing. Keep an image and infrastructure definition ready so a new region can be used if the preferred pool is full. Treat the published hourly rate as a starting point; storage, networking, taxes and support can change the invoice.
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Use frequent, provider-independent checkpoints and an automatic resume path. Test interruption handling under load, including re-queuing, failed checkpoint uploads and partial workers. Calculate the expected cost of lost work, not just the discounted rate. A spot job that restarts repeatedly can exceed on-demand cost and miss its deadline.
Reserved and private-cluster planning
Reservations are justified when utilization is high and demand is predictable. Before signing, model the lowest realistic utilization, planned maintenance, hiring or project delays, and the cost of moving away. For a private cluster, define hardware replacement time, access controls, support hours and whether the cluster is physically isolated.
Model the effective cost
Compare the cost of a completed result, not only dollars per GPU-hour. A practical model is:
effective cost = GPU time + storage + snapshots + inter-region traffic + egress + orchestration + support + software licenses + idle reservation + migration work + expected interruption cost
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Use the unit that matches the business
- Training: cost per completed step, epoch or trained token.
- Rendering: cost per accepted image, frame or minute of output.
- Inference: cost per million tokens, image or successful request at the target latency.
Include the host and non-GPU time if the provider bills a full node. Add checkpoint storage and retrieval fees, image-transfer time, observability, scheduler overhead and engineering work required to operate another region. For a reservation, charge unused hours to the workloads that were expected to consume them.
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Published capacity and pricing evidence
The following figures are public list information, not quotes for a production commitment. CoreWeave figures were observed on October 18, 2026; region, taxes, storage and networking can change the effective rate. Regional inventory, queue times, negotiated enterprise rates, outage history and provider-specific failover performance are not fully published in the cited provider materials, so request current, region-specific terms.
| Offering | Published hardware or target | Published price or commitment | What is not established by the public figure |
|---|---|---|---|
| CoreWeave eight-GPU NVIDIA HGX H100 | Eight GPUs; 80 GB VRAM per GPU | $49.24 per hour on-demand; $19.71 per hour spot (list prices observed 2026-10-18) | Region, queue time, storage and network charges, interruption behavior and negotiated rates |
| CoreWeave eight-GPU NVIDIA HGX H200 | Eight GPUs; 141 GB VRAM per GPU | $50.44 per hour on-demand; $20.93 per hour spot (list prices observed 2026-10-18) | Region, queue time, storage and network charges, interruption behavior and negotiated rates |
| Lambda Private Cloud | Bare-metal, single-tenant clusters with customer-only access and 24×7 hardware support | Clusters of 1,000+ NVIDIA HGX B200 GPUs reserved for one to three years; hyperscale clusters of 2,000–64,000 NVIDIA GPUs reserved for three to ten years | No public hourly price in the cited documentation; placement, portability, replacement time and regional inventory require a quote |
| NVIDIA DGX Cloud ACE | Service and capacity targets measured per Accelerate Computing Environment | 99% service availability and 95% capacity availability per calendar month | Targets are not a promise of a particular GPU SKU in a particular region; remedy and exclusions are defined by the SLA |
Read a GPU-cloud SLA as an operations document
NVIDIA’s Cloud Services SLA, last modified November 5, 2025, defines two separate measures for DGX Cloud ACE: “Service Availability: 99%; Capacity Availability: 95%” per calendar month. Service availability is measured every 15 minutes, while capacity availability is measured every 60 minutes. Gaps shorter than those intervals are excluded.
Service availability versus capacity availability
Service availability asks whether the cloud service is functioning. Capacity availability asks whether the provider can supply the capacity covered by the offering. A control plane can be reachable while the GPU pool is exhausted, so insist that both measures appear in your contract.
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Check the measurement rules
- Identify the calendar-month window and the exact measurement interval.
- Define the start and end of an outage, including partial-node and degraded-performance events.
- List scheduled maintenance, force-majeure events and customer-caused exclusions.
- State whether queue time, failed provisioning and unavailable substitute SKUs count as capacity failures.
Understand the claim process and remedy
Under the cited NVIDIA SLA, a claim must be submitted within two months and include incident timing, the affected offering, users and troubleshooting details. A validated failure produces a credit equal to the interruption scope, usable only toward a future DGX Cloud order term. A credit is not cash compensation and normally will not cover a missed launch, delayed release, lost revenue or downstream penalties.
Negotiate capacity-shortfall remedies separately from software-service uptime remedies. Specify whether the remedy is a refund, future-use credit, term extension or replacement capacity, and assign a monetary value to each.
Contract clauses that prevent unpleasant surprises
Capacity definition
Name the GPU generation, VRAM, node size, interconnect class, region and minimum quantity. Define an acceptable substitute by measurable memory, bandwidth and benchmark criteria. State how quickly failed nodes must be replaced and whether replacement can be moved to another region.
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Reservation mechanics
Clarify start date, ramp schedule, take-or-pay obligations, cancellation rights, rollover, regional portability and the treatment of unused hours. Ask whether a reservation follows your account or is tied to a physical cluster.
Data and exit
Set data-residency requirements for training data, checkpoints and logs. Document export formats, object-storage access, egress pricing, deletion timelines and assistance for moving images, drivers, containers and infrastructure definitions.
Support and escalation
Put response and restoration targets in writing. Identify 24×7 coverage, severity levels, named escalation paths and the information required to open an incident. Hardware support that is available 24×7 is different from a guaranteed time to restore GPU capacity.
Software licensing
NVIDIA states that virtual GPU software can be purchased per concurrent user or per GPU as an annual subscription or perpetual license. A current SUMs contract supplies support, upgrade and maintenance access. Confirm which license metric applies, whether it transfers between hosts, and what happens when a reserved cluster is expanded or replaced.
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1. Make checkpoints provider-independent
Write checkpoints to object storage that can be accessed from another provider or region. Keep enough history to recover from a corrupt latest checkpoint, and encrypt with keys and credentials that are not locked to one control plane.
2. Measure restore time
Periodically launch a clean environment, install the pinned driver and framework versions, download a checkpoint and resume training. Record time to first useful step, data-transfer throughput and any manual actions. A backup that has never been restored is an assumption, not a recovery plan.
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3. Reproduce the environment
Store container digests, operating-system images, driver compatibility, Python or system dependencies, configuration and infrastructure-as-code in version control. Avoid relying on an interactive shell or an unrecorded local modification.
4. Keep a warm or documented secondary path
For deadline-sensitive jobs, maintain a second provider or region with tested quotas. For less critical work, a documented cold path may be sufficient if the measured restore time fits the allowed delay. Verify that the alternate path offers the required GPU memory and interconnect, not merely the same nominal GPU family.
5. Automate interruption handling
Use an idempotent launcher that detects a failed worker, validates the newest checkpoint, re-creates the cluster and resumes from the last consistent state. Alert on checkpoint age, failed uploads, queue time and GPU health.
6. Separate technical and business recovery objectives
Define a maximum tolerable data loss interval and a maximum tolerable delay. The provider SLA may issue a credit while your business still misses a launch window, so the recovery design must cover the gap.
Questions to ask during a provider evaluation
- Which regions can supply the exact GPU, node size and interconnect today, and what is the normal queue time?
- Is the quoted capacity physically isolated, logically reserved or simply a scheduling priority?
- What happens if the named SKU is unavailable after a reservation begins?
- Can capacity move between regions, GPU generations or accounts without a new contract?
- What are storage, snapshot, inter-region transfer and egress rates?
- How are spot interruptions announced, and does billing stop immediately?
- What are hardware replacement and support response targets?
- Does the SLA measure service availability, capacity availability or both?
- What events are excluded, how long are measurement gaps, and how soon must a claim be filed?
- Is the remedy cash, credit, term extension or substitute capacity?
- Can you export checkpoints, images, logs and infrastructure definitions without proprietary conversion?
- Which data-residency, access-control and audit requirements apply to your workload?
A practical selection decision
| Your situation | Starting choice | Controls to add |
|---|---|---|
| Model architecture or demand is still changing | On-demand capacity | Portable images, short benchmarks and no long reservation until utilization is proven |
| Long training jobs can restart and have frequent checkpoints | Spot capacity | Automatic resume, interruption testing and an expected-interruption-cost calculation |
| Production inference has steady traffic and a strict latency target | Reserved or private capacity sized for peak plus failure headroom | Capacity SLA, replacement commitment, second-region failover and egress review |
| Large single-tenant training program | Private cluster reservation | Take-or-pay model, hardware isolation, portability, data residency and migration terms |
| Launch date cannot slip | Primary reservation plus independently tested backup | Provider-independent checkpoints, quota confirmation and a rehearsed cutover |
Procurement checklist
- Freeze a workload specification with memory, batch, sequence, throughput, latency, parallelism and storage requirements.
- Benchmark the smallest viable topology and at least one higher-capacity option using production-shaped data.
- Obtain region-specific quotes that itemize GPU, storage, snapshots, networking, support and licenses.
- Compare on-demand, spot and reservation scenarios using utilization and interruption assumptions.
- Verify quota, queue time and physical placement for the exact GPU configuration.
- Negotiate service and capacity availability as separate commitments.
- Define exclusions, evidence, claim deadlines and the value of each remedy.
- Test checkpoint restore and secondary-provider provisioning before committing a critical run.
- Review reservation portability, exit costs and data-export procedures with legal and security teams.
- Set alerts for capacity, spend, checkpoint age, queue time and GPU failures.
Choose the configuration that completes the required work at an acceptable risk-adjusted cost. A lower GPU-hour price is not a saving if the topology slows the job, the reservation sits idle, or an outage leaves you unable to resume.
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