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Choose a cloud GPU provider by testing whether its exact GPU, host, network, regional capacity, software stack, and purchasing terms fit your workload—not by picking the lowest advertised GPU-hour price. Before committing, verify the specific instance and region you can actually obtain, benchmark a representative job, and compare the full cost per completed training step, token, image, or inference.
What should you define about your AI workload first?
Write down what the workload must do before comparing providers. “AI training” or “inference” is too broad to identify the right GPU: a large model’s memory needs, acceptable precision, traffic pattern, and ability to tolerate interruptions can all change the best choice.
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Google Cloud’s guidance is to match GPU choice to a workload’s computational characteristics, memory requirements, precision formats, and scalability needs. Its machine-series guidance also distinguishes workload categories such as frontier-model training, high-throughput serving, graphics, and high-performance computing. Use those categories as a starting point, then validate them against your own job.
Make a workload card
- Job type: training from scratch, fine-tuning, batch inference, or interactive serving.
- Model and memory: model size, peak GPU memory in use, and whether the workload needs one GPU or several.
- Precision: the formats the model and software can use, such as FP32, BF16, FP16, FP8, INT8, or INT4 where supported.
- Service target: expected concurrency and the latency or throughput target that defines an acceptable result.
- Topology: single-host or multi-host execution, and the number of GPUs needed together.
- Data constraints: required region or residency, data location, and expected storage and transfer needs.
- Interruption tolerance: whether work can pause, checkpoint, restart, or be discarded if capacity is reclaimed.
This card makes comparisons more concrete. A provider that looks attractive for a restartable batch job may not fit an interactive service that must meet a latency target, and an instance with enough aggregate GPU memory may still fail to fit a model on each individual GPU.
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Which GPU and instance specifications matter beyond the model name?
Compare the whole instance, not just the GPU model. GPU memory, precision support, CPU and system RAM, local and attached storage, GPU count, and the interconnect all affect whether the workload fits and how quickly it runs.
Read the instance as a system
- GPU memory and count: check memory per GPU as well as total memory across the host. Do not assume memory is pooled across devices.
- Numeric-format performance: confirm the GPU and your framework support the precision your model can use. A capability on a product page does not, by itself, establish that your workload can use it efficiently.
- Host resources: compare CPU and system RAM. Data preparation or input pipelines can limit a GPU job even when GPU capacity is ample.
- Storage: check local NVMe capacity and the bandwidth of any attached storage you plan to use. Include the work of loading datasets and writing checkpoints.
- Multi-GPU fabric: check NVLink, NVSwitch, or the provider’s equivalent within a host, along with RDMA or EFA support between hosts where needed.
A concrete AWS example is the P5.48xlarge configuration described in Amazon EC2 documentation: eight H100 GPUs, 640 GB of total GPU memory, 3,200 Gbps EFAv2, 900 GB/s NVSwitch peer-to-peer bandwidth, and eight 3.84 TB NVMe devices. These are configuration-specific figures, not promises for every P5 instance or every job. Confirm the exact instance specification and the provider’s current documentation before designing around them.
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How do you check multi-GPU and multi-node performance?
Run your representative workload on the intended topology and measure its scaling; a network headline is not a substitute for a job benchmark. Multi-GPU training depends on the fabric, placement, drivers, and framework path actually available to the workload.
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Benchmark the job, not a synthetic peak
- Start with one GPU. Record throughput, latency where relevant, utilization, and the time to complete a known amount of work.
- Repeat at two, four, and eight GPUs where those configurations are relevant and available. Compare the throughput gain with the added GPU count to see whether scaling is useful for your job.
- Measure communication. For distributed training, record all-reduce time or the equivalent communication cost. For multi-node work, test the actual placement and network path you expect to use.
- Measure the data path. Observe dataloader performance and storage throughput while the job runs, rather than assuming storage can feed the GPUs at the required rate.
- Test checkpoint and recovery. Record checkpoint duration and the time and work required to resume after a node or job interruption.
- For serving, test concurrent traffic. Measure tail latency as well as average throughput under the concurrency you expect.
Capture GPU utilization and cost alongside those results. A configuration with higher raw throughput may still be a worse fit if it costs more per completed unit or spends substantial time waiting on input, communication, or recovery. Use the same model, data, software configuration, and completion criteria when comparing providers.
How can you confirm GPU capacity in the region you need?
Check the exact GPU SKU, region, zone, quota, acquisition method, and reservation process before finalizing the architecture. A catalog entry shows that a product exists; it does not guarantee that your account can obtain it when you need it.
Availability can differ across GPU families and acquisition modes. Google Cloud documents different combinations of on-demand, Spot, Flex-start, and reservation availability across families including A3 Ultra, A3 Mega, A3 Edge, A2, and G2. Some A3 Ultra and other newer families have restrictions on on-demand or reservation acquisition. Do not transfer the availability rules for one family to another.
Questions to resolve with the provider
- Is the exact GPU instance available in the required region and zone for your account?
- What quota applies, and what steps and lead time are needed to raise it?
- Can capacity be reserved, and what does the reservation cover?
- Which purchase modes are available for that family in that location?
- Can you expand capacity if the workload grows, or does the design depend on a single constrained location?
Get answers tied to the SKU and location in writing. If capacity uncertainty would delay a launch or interrupt a service, treat it as an architecture and purchasing risk—not a detail to resolve after development.
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How should you compare the total cost of a cloud GPU workload?
Compare the cost of completing useful work, not only the displayed GPU-hour price. The bill can include the VM as well as its GPUs, storage, data movement, orchestration, idle time, observability, support, licensing, checkpoint transfers, and recovery work.
Google Cloud states that each GPU adds to the cost of an instance in addition to the machine type. Its pricing documentation covers regional GPU prices, sustained-use and committed-use discounts, reservations, and Spot pricing. The applicable price can therefore depend on region, machine type, commitment, reservation, and purchase mode.
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For each candidate, estimate the cost over the same completed unit of work. For example, divide the full run cost by completed training steps, processed tokens, generated images, or successful inferences. Include time spent waiting, restarting, transferring checkpoints, and operating the service when those costs apply. State your assumptions so another person can reproduce the comparison.
Google Cloud’s pricing documentation lists Spot discounts of 60–91% off corresponding on-demand prices for most machine types and GPUs. That is a documented discount range, not a guaranteed price for every GPU, location, or time. Google also notes that Spot prices can change and that discounts vary by machine type. A lower hourly rate is not automatically lower cost per completed job if interruptions add work or delay completion.
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Cost items to include
- GPU and VM charges for active work, warm capacity, and idle periods.
- Storage used by datasets, checkpoints, snapshots, and intermediate results.
- Ingress and egress charges, including checkpoint transfer and data movement between services.
- Orchestration, monitoring, support, and any required software licensing.
- Expected time and compute lost to interruption, failure, restart, or recovery.
- Any reservation or commitment cost and the utilization needed to make it worthwhile.
Use the same workload duration, region, data assumptions, and purchasing mode for every candidate. Otherwise, a price comparison can mix unlike products and obscure the actual trade-off.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you use on-demand, reserved, committed, or Spot capacity?
Choose the purchase mode according to demand certainty, capacity risk, utilization, and interruption tolerance. The cheapest advertised mode is not necessarily the right one if the workload cannot handle its constraints.
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|---|---|---|
| On-demand | Demand is uncertain or a production workload cannot tolerate interruption. | Confirm the exact region’s availability, quota, and price. |
| Reservation or commitment | Utilization is stable and you understand the capacity and contract risk. | Verify what capacity is reserved, for how long, and whether the commitment fits expected use. |
| Spot or preemptible | Experiments or batch jobs can checkpoint and restart. | Confirm interruption notices, maximum runtime, replacement capacity, and framework progress preservation. |
For interruptible capacity, test the recovery path rather than assuming a checkpoint is sufficient. A useful operational test is to interrupt a representative run, restore from its checkpoint, and measure both the lost work and time until useful processing resumes. Include those costs when evaluating Spot or preemptible savings.
What security, operations, and support details should you verify?
GPU product pages do not establish whether a service meets your organization’s security, residency, support, or availability requirements. Get answers for the exact provider, region, and instance family you plan to use.
- Identity and access: confirm integration with your identity system and the controls available for administrators, workloads, and operators.
- Network and data protection: check private networking, encryption, key management, and logging options.
- Platform operations: understand image and driver lifecycle, vulnerability response, maintenance windows, and how tenant isolation is described.
- Data location: verify data-residency commitments and where relevant data and backups may be stored or processed.
- Support and service terms: ask how severity levels are defined, how support is reached, what the SLA covers, and how credits are handled.
Record the answers and the applicable contract or service documentation. A broad statement about a provider’s platform is not a substitute for terms that apply to the GPU service and region in your design.
How do you compare providers without overvaluing a benchmark or catalog?
Use explicit axes and evidence from your own workload. Compare GPU and memory fit, single- and multi-node performance, regional capacity, effective cost, interruption risk, data movement, security and compliance, orchestration integration, support, and contract flexibility.
A CAST AI 2025 benchmark reported that the share of regions with any GPU instance was 85% for AWS, 58% for Google Cloud, and 76% for Azure under its methodology. Treat those figures as directional and dated: they describe regions with any GPU instance in that benchmark, not availability of a particular SKU, capacity for a particular account, or a current guarantee. A regional percentage cannot replace checking the exact product and location you need.
Keep a comparison record with the workload card, configuration tested, region, purchase mode, benchmark results, full-cost assumptions, and written capacity and support answers. That record makes a provider decision auditable and helps identify whether a result reflects hardware, network, location, pricing, or an operational constraint.
Frequently Asked Questions
Does the 640 GB figure on an eight-GPU instance mean one model can use all 640 GB as one pool?
No. The AWS P5.48xlarge figure is aggregate GPU memory across eight H100s; it does not mean a single GPU has access to all of it. Confirm per-GPU memory and whether your software can distribute model state across devices.
How do I compare cloud GPU prices for inference?
Use the same model, region, precision, and concurrency target, then divide the full run or serving cost by successful inferences. Include idle capacity and any storage or data-transfer charges that apply to that test.
Can I treat a provider’s regional GPU percentage as proof that my account can get a specific GPU?
No. The CAST AI 2025 percentages count regions with any GPU instance under that benchmark’s methodology. They do not establish stock, quota, or acquisition options for a particular SKU in your account.
Quick Recap
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.
