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Compare GPU clouds against one specific workload and machine shape—not a headline hourly rate or a broad uptime claim. Verify that the exact GPU count is obtainable in your target region and zone, determine whether the network fabric can support your training job, and separate fast temporary storage from durable datasets and checkpoints. AWS, Google Cloud, Runpod, and CoreWeave publish useful specifications, but those specifications do not establish that capacity is immediately available or that one provider will perform better on your workload.
What should I compare first?
Start with the job you need to run and record its requirements before comparing providers. A useful comparison holds the workload constant: the same GPU generation and count, region, interconnect needs, dataset size, checkpoint schedule, and expected runtime. Otherwise, a cheaper hourly rate may be offset by a longer queue, slower data access, transfer charges, or retries after preemption.
For each candidate, collect three groups of facts:
- Availability: exact GPU SKU and count, region and zone, quota, reservation or capacity-block terms, expected start time, preemption behavior, failover plan, and the scope of any SLA.
- Networking: bandwidth and topology for the chosen shape, whether the figure is per GPU, per VM, or aggregate, RDMA support, NIC count, and required software or machine configuration.
- Storage: local scratch capacity and performance, durable block or file storage, object storage, backup and snapshot behavior, and data-transfer or egress terms.
Ask providers to quote and confirm these details for the same deployment shape. A catalog entry establishes that a configuration is listed or geographically eligible; it is not proof that the requested capacity can start when you need it.
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How can I tell whether GPU capacity will be available?
Availability is a combination of capacity, placement, and timing—not just a reported uptime percentage. Check the exact model, count, zone, quota, reservation mechanism, lead time, and what happens if the host or zone becomes unavailable.
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Check placement and quota
Record the requested GPU SKU, number of GPUs, region, and zone. Then confirm the relevant quota and whether it applies globally or regionally. Google Cloud documents global and regional GPU quotas; a quota is a limit on what an account may request, not a promise that physical GPUs are free to allocate at a particular moment.
Google Cloud’s location information marks some A3 and A2 locations as limited capacity and says account-team contact may be required. Treat a location listing as a statement about geographic eligibility, not as a live capacity guarantee. Ask the provider to confirm the specific shape and minimum start time in writing.
Distinguish reservations from on-demand requests
Ask what mechanism, if any, protects the capacity you need. Google says, “Reservations provide high assurance of capacity for zone-specific resources, including GPUs.” That assurance is zone-specific; it does not mean a reservation in one zone automatically supplies replacement GPUs in another zone.
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Ask what “failover” actually means
Find out whether replacement capacity is reserved, merely requested on a best-effort basis, or unavailable if the original zone fails. Google Cloud warns that GPU failover can be best effort without zonal redundancy. Its Compute Engine SLA coverage also depends on a generally available GPU model being present in more than one zone in the region. Do not infer GPU-specific SLA coverage from a general cloud SLA without checking those conditions.
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Ask how spot or other preemptible capacity is reclaimed, how much notice is provided, and whether the workload can restart elsewhere. For long-running training, model the possibility of a lost run or a delayed restart; for short inference jobs, the consequences may be different. Do not treat an advertised uptime percentage as a guarantee that a particular GPU SKU will be obtainable on demand.
Which networking specifications matter for training?
For distributed training, compare the GPU-to-GPU fabric and topology—not only the VM’s advertised network bandwidth. All-reduce performance can depend on the interconnect, RDMA support, topology, NIC configuration, and software stack, so a provider’s maximum bandwidth is a specification to verify against your exact shape rather than a cross-provider benchmark.
Single-GPU and multi-node workloads have different needs
For single-GPU inference, ordinary VPC bandwidth and latency may be more relevant than a high-end multi-node fabric. For multi-node training, ask about GPU-to-GPU communication, RDMA, NIC count, topology, and collective-communication software. A high aggregate network number alone does not show how much bandwidth one GPU or one training job will actually receive.
Compare published accelerator configurations carefully
| Provider and configuration | Published network and topology details | What to verify |
|---|---|---|
| AWS P5.48xlarge | Up to 3,200 Gbps EFAv2; GPUDirect RDMA; 900 GB/s NVSwitch peer-to-peer; eight H100 GPUs; eight 3.84-TB NVMe SSDs. | Confirm the figure and configuration for the exact instance, EFA generation, software stack, and placement you will use. These are published specifications, not an independent workload benchmark. |
| AWS P5e/P5en H200 variants | H200 variants with the same headline network class described in the AWS accelerated-computing documentation, but different EFA generations. | Check the exact variant and EFA generation; do not assume that similarly named P5 shapes have identical details. |
| Google Cloud A4 and A3 Ultra | Maximum network bandwidth up to 3,600 Gbps with GPUDirect RDMA. | Confirm the selected shape, how the maximum is counted, and that the required RDMA path is available in the target zone. |
| Google Cloud A3 Mega | Up to 1,800 Gbps with GPUDirect-TCPXO. | Confirm that the workload and software support the specified path and shape. |
| Google Cloud A3 High | Up to 1,000 Gbps with GPUDirect-TCPX. | Check the exact configuration and whether it meets the workload’s communication needs. |
| Runpod H100/H200/B200 clusters | Provider-published comparison reports 3,200-Gbps networking and InfiniBand or RoCE v2 options. | Confirm cluster size, region, fabric option, and the units and scope of the published figure at quote time. |
| CoreWeave | Official pricing lists current GPU families, but the cited pricing information does not establish a comparable network-fabric figure. | Request the fabric, topology, and network terms for the quoted GPU shape. |
Google defines GPUDirect RDMA as a technology that allows a network interface card to access GPU memory over PCIe, bypassing host CPU and system memory. That description explains the intended data path; it does not by itself establish end-to-end job performance. Confirm whether the advertised feature requires a particular machine shape or software configuration.
For a fair comparison, run the same short representative job on the shortlisted configurations and measure all-reduce bandwidth and latency. Record the actual GPU count and placement along with the result; a number from one topology should not be presented as a general provider-wide result.
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Where should datasets, scratch files, and checkpoints live?
Use at least three storage layers in your design: local NVMe or Local SSD for fast temporary work, durable block or file storage for recoverable working data, and object storage for datasets and checkpoints where it fits your access pattern. The key distinction is recoverability: local accelerator-host storage may disappear or become inaccessible when a host is stopped or replaced.
Use local storage for disposable work
Local NVMe and Local SSD are useful candidates for cache, shuffle data, and scratch space. AWS publishes local instance storage alongside EBS bandwidth for accelerated-computing instances, making both parts relevant when comparing a P5 configuration. Compare the local capacity and the durable-storage path separately rather than treating them as one pool.
Google Cloud states that Compute Engine stops instances with attached GPUs during host maintenance and that attached Local SSD data is lost after the stop. Do not keep the only copy of a dataset, checkpoint, or other state you cannot recreate on that storage. Plan to repopulate scratch space after a stop or replacement.
Make durable state recoverable
Put source datasets and checkpoints on durable storage appropriate to their access pattern, and verify how the GPU hosts reach it. Compare throughput, IOPS, network path, replication or durability terms, snapshot and backup behavior, and transfer or egress fees. Also check whether recovery requires remounting, copying data back to local scratch, or restoring a snapshot; each can affect restart time.
For each quote, identify which layer provides the stated capacity and performance. A large local NVMe allocation does not tell you the throughput, durability, or cost of a separate networked filesystem or object store. Likewise, durable capacity does not guarantee that it can feed a large GPU job fast enough.
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Use storage targets as questions, not provider guarantees
NVIDIA’s Requirements for AI Clouds v2.1 gives guidance targets: home-directory availability above 99%, a high-speed-storage service-level objective of 99.99% over a 30-day rolling period, and durability above 99.999% per PB for high-speed filesystems. These are requirements guidance, not evidence that any given provider meets them or a substitute for reviewing the actual contract. Ask providers to state their own commitments and the measurement scope.
How do AWS, Google Cloud, Runpod, and CoreWeave differ?
The published information points to different comparison strengths, but it does not establish a universal winner. AWS and Google Cloud publish detailed infrastructure specifications and capacity mechanisms; Runpod’s published comparison highlights billing and orchestration options; CoreWeave’s pricing information exposes GPU families and storage fields. Verify regional availability, contract terms, and the exact deployment details before choosing.
| Provider | What the cited material makes useful to compare | Questions to settle before purchase |
|---|---|---|
| AWS | P5-family EFA, GPUDirect RDMA, NVSwitch, local NVMe, and EBS figures; Capacity Blocks and reservations are relevant to capacity planning. | What capacity mechanism applies to the target shape and dates? What are its costs and terms? Is the quoted P5 variant the one whose published network and storage figures you used? |
| Google Cloud | Regional and quota controls, reservations, and detailed A3/A4 networking information. | Is the target location limited capacity? Does the requested GPU model meet the conditions for the relevant SLA? What happens on maintenance or zone failure? |
| Runpod | Provider-published comparison reports per-second billing, 3,200-Gbps networking on H100/H200/B200 clusters, InfiniBand or RoCE v2, and managed Slurm or Ray. | Is the quoted cluster size available in the needed region? Which fabric and orchestration options apply to that quote? Confirm billing and availability terms directly. |
| CoreWeave | Official pricing lists current GPU families, local-storage capacity, on-demand and spot fields, and regional pricing views. | Ask for committed-capacity terms, network fabric, and durable-storage conditions in the same quote; pricing fields alone do not establish those terms. |
Runpod’s billing and orchestration details are provider-published comparison claims; CoreWeave’s listed price fields do not establish a comparable network specification in the cited material. Treat both as prompts for a quote, not as substitutes for confirming the actual cluster and service terms. For all providers, regional and contractual details can change what is available to your account.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I run a fair procurement test?
Use a single representative workload and a written test plan. The purpose is to expose capacity, fabric, storage, and recovery differences that a price sheet or maximum-bandwidth figure cannot answer.
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- Run a representative job. Measure all-reduce bandwidth and latency for distributed training, storage read and write throughput for the dataset path, checkpoint restore time, and recovery after a controlled failure or restart. Use the same workload settings and data size for each candidate.
- Normalize the full cost. Include GPU hours, durable storage, snapshots, ingress and egress, idle reservations, and retries after preemption. Compare the cost for a completed unit of work, not just the displayed GPU-hour rate.
- Clarify specification units and dependencies. Ask whether network figures are per GPU, per VM, or aggregate, and whether RDMA depends on a particular shape or software stack. Record the exact configuration used for each test.
- Document failure behavior. Get written answers for host maintenance, zone outage, quota exhaustion, spot preemption, and support escalation. Specify whether the response is automatic, best effort, or dependent on separately reserved capacity.
- Set acceptance thresholds before comparing results. Choose the maximum acceptable start delay, minimum training throughput, maximum restore time, and recovery behavior for your application. The provider specifications cited here do not define universal pass/fail thresholds for every workload.
What can the published figures prove—and what can’t they?
The AWS and Google Cloud bandwidth figures above are published specifications, and Runpod’s cluster figure is a provider-published comparison claim. They can help identify configurations worth testing, but they are not independent cross-provider benchmarks. The available published information does not establish a comparable availability percentage across these providers.
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Before relying on any number, establish its scope: model and shape, per-device or aggregate units, region, network generation, and any required software. Apply the same discipline to storage metrics and SLAs: confirm what is measured, over what period, and which service or resource is covered. Where a provider’s published materials do not state a comparable value, request it rather than inferring one from a neighboring product or general service page.
Which source documents support these comparisons?
The specifications and qualifications in this guide are based on the relevant provider documentation and the NVIDIA guidance available on October 25, 2026: Amazon Web Services’ accelerated-computing documentation; Google Cloud’s GPU locations, networking and GPU machines, and About GPU instances documentation; Runpod’s provider-published comparison; CoreWeave’s official pricing information; and NVIDIA’s Requirements for AI Clouds v2.1. No URLs were provided for these source references, so they are named here without reconstructed links.
Frequently Asked Questions
Does a GPU cloud uptime SLA mean my H100 or H200 will be ready when I request it?
No. An uptime commitment and the ability to allocate a specific GPU shape at a particular time are different questions. Ask for capacity confirmation and the SLA scope for the exact model, zone, and reservation you plan to use.
How should I compare per-second billing with hourly GPU prices?
Calculate cost for the same completed job, including startup, data movement, storage, idle reservation time, and any retries. A smaller billing increment matters only if the workload’s actual billed duration and surrounding charges make the total lower.
Is a listed GPU location enough to plan a production launch?
Not by itself. Location catalogs can describe eligible zones without confirming live capacity for your account; obtain a provider confirmation for the required shape, count, and start window.
What does the NVIDIA 99.99% storage target mean for my provider contract?
It is guidance in NVIDIA’s AI-cloud requirements, not a contractual promise from a cloud provider. Look for the provider’s own SLO or SLA, including the service covered, measurement window, exclusions, and remedy.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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