No cloud wins for every workload, and published specifications can’t prove which one is fastest or cheapest for yours. The reliable method has four steps. Work out the memory and scale your model needs. Shortlist configurations that meet those needs. Confirm you can actually get them in your region. Then benchmark the finalists with your own model and software stack, and compare the full billed cost of a finished run, not the hourly GPU rate.
This guide walks through that method using AWS, Microsoft Azure and Google Cloud documentation as concrete examples. It also marks where public documentation stops being useful, so you know which questions only a test can answer.
Step 1: Define the workload before you look at any provider
Training from scratch, fine-tuning, batch inference and online inference stress hardware in different ways. Write down these inputs first, because every later comparison depends on them:
- Task type: pre-training, fine-tuning, batch inference or latency-sensitive online serving.
- Model and version: parameter count, architecture, and any quantization you plan to use.
- Precision: FP32, BF16/FP16, or lower-precision formats.
- Batch size, context length and concurrency: these drive memory use and, for serving, latency.
- Data volume and location: where the training data or model artifacts already live.
- Targets: throughput, end-to-end latency, deadline, and budget ceiling.
- Constraints: required region, data-residency or compliance rules, and the frameworks your team already uses.
AWS’s Deep Learning AMI guidance on recommended GPU instances puts the principle plainly: “The size of your model should be a factor in choosing an instance.” It also advises choosing a configuration with enough RAM when the model exceeds what a smaller one offers.
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Step 2: Estimate memory to set your minimum accelerator size
Memory sets the floor for everything else. These are approximations, so leave headroom, and treat them as a screening step rather than a sizing guarantee.
- Inference weights: parameters × bytes per parameter. A 7-billion-parameter model in 16-bit precision needs roughly 14 GB for weights alone. A 70-billion-parameter model needs roughly 140 GB. Add memory for the attention key-value cache, which grows with context length and the number of concurrent requests, plus runtime overhead.
- Full training or fine-tuning: a commonly cited rule of thumb for mixed-precision training with an Adam-style optimizer is around 16 bytes per parameter before activations. That is about 112 GB for a 7B model. Parameter-efficient methods, optimizer sharding and activation checkpointing can reduce this substantially.
Now match the result to real instance specs. AWS’s EC2 accelerated-computing documentation lists p5.4xlarge with one H100 (80 GiB of accelerator memory) and p5.48xlarge with eight H100 GPUs (640 GiB combined). It also lists P5e configurations with H200 GPUs. On those numbers, a 70B model in 16-bit precision can’t fit on one 80 GiB H100 without quantization or sharding. It can fit on an eight-GPU node, but then the link between the GPUs starts to matter.
Step 3: Shortlist configurations from each provider
The table below gives one documented example per provider. Each is a data point for what to compare, not a recommendation or a ranking.
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| Provider | Documented example | What it helps you compare | Qualification |
|---|---|---|---|
| AWS | EC2 accelerated computing covers NVIDIA GPU families (including P5/P5e), Inferentia inference accelerators (for example Inf2) and Trainium training accelerators. | GPUs versus purpose-built accelerators; instance shape; fit for training or inference. | Check exact family, region, account quota and current price. The documentation is not a cross-provider benchmark. |
| Microsoft Azure | ND H100 v5: eight H100 GPUs per VM, NVLink 4.0, up to 3.2 Tbps interconnect bandwidth per VM and a dedicated 400 Gbps InfiniBand connection per GPU. Azure positions it for high-end deep learning and scale-up/scale-out work. | Multi-GPU topology and distributed-training networking. | Vendor specification, with no source date on the page reviewed. Azure ML documentation warns that some GPU VM series aren’t available in all regions. |
| Google Cloud | Compute Engine documents GPU machine types for AI/ML and publishes a GPU pricing page with per-model prices, commitment-discount columns and dynamic Spot pricing. | Machine configuration, regional choice and pricing model. Google’s guidance separates general GPU workloads from larger, tightly synchronized cluster needs. | Prices and Spot discounts change. One machine type’s price is not a workload cost. |
Include non-GPU accelerators when they fit
A GPU-only comparison can leave out options. AWS, for example, sells Inferentia for inference and Trainium for training alongside its GPU instances. These can be worth testing, but only if your framework, model architecture and tooling support them. If your code depends on CUDA-specific libraries, they may not be a drop-in alternative. Treat them as extra finalists in the benchmark, not as assumed savings.
Step 4: For multi-node training, compare the network, not just the GPU
A single-GPU or single-node job mostly cares about accelerator memory and speed. Distributed training adds a second question: how fast do GPUs exchange gradients and activations?
- Inside a node: the GPU-to-GPU link, such as the NVLink 4.0 that Azure documents for ND H100 v5.
- Between nodes: the cluster fabric, such as the per-GPU 400 Gbps InfiniBand connection Azure documents for the same family.
- Cluster support: whether the provider offers a placement or cluster arrangement that keeps nodes close together. Google distinguishes larger synchronized-cluster needs from ordinary GPU workloads in its guidance, so read that section for your intended scale.
Check the equivalent specifications for any other candidate. A slower fabric can leave expensive GPUs idle waiting on communication. Spec sheets can’t tell you how much that matters, because it depends on model size, parallelism strategy and framework. The benchmark will.
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Inference-only workloads that fit on one GPU usually care far less about inter-node networking. Memory, per-request latency, batching efficiency and scale-out behavior matter more.
Step 5: Confirm region, quota and real capacity
A catalog entry is not a reservation. Before you invest in a comparison, verify that each finalist can be provisioned where and when you need it:
- Region: Azure ML documentation says some GPU VM series may not be available in every region, and it points users to regional product availability and supported-size checks. Treat the same caution as applying to any provider.
- Quota: new accounts commonly start with low or zero GPU quota, so file quota increases early. Approval time varies by provider and account.
- Capacity for your dates and scale: try to provision the exact size in the exact region. If you need a large cluster for a fixed window, ask the provider about reservations or committed capacity rather than assuming on-demand will be there.
- Data location: if your data or compliance rules pin you to a region, that may rule out candidates before any price comparison.
Step 6: Estimate the full cost of the run
An hourly GPU price is only one line. A fair estimate uses equivalent assumptions across providers:
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- Accelerator time: the number of accelerators multiplied by the measured wall-clock hours, not the advertised speed.
- Host resources: CPU, system memory and local disk bundled with or billed alongside the instance.
- Storage: datasets, checkpoints and model artifacts, plus any high-performance file system the job needs.
- Networking: data transfer between regions, to the internet or between clouds, which can be significant when moving large datasets or serving traffic.
- Pricing model: on-demand, Spot/preemptible, or a committed-use discount. Google’s pricing page, for example, shows commitment-discount columns and describes dynamic Spot prices.
- Interruption cost: discounted interruptible capacity is cheaper per hour but can lose progress. Count checkpoint frequency, restart time and any deadline risk.
- Idle and setup time: provisioning, environment build, data loading and any GPUs that sit underused during a run.
A simple form is: total cost = (accelerator-hours × rate) + host and storage charges + data transfer + rerun cost from interruptions. The illustration below uses made-up numbers purely to show the method.
| Hypothetical finalist | Rate per GPU-hour | Measured GPU-hours for the same job | Compute cost |
|---|---|---|---|
| Option A | $4.00 | 1,000 | $4,000 |
| Option B | $3.00 | 1,500 (slower interconnect) | $4,500 |
In this invented case the cheaper hourly rate loses once measured runtime is included, and storage, transfer and restarts haven’t even been added yet. Only your own benchmark can tell you whether a real comparison looks like this.
Prices are volatile, so look them up live for your region, currency, machine configuration and pricing conditions. As one example of how a listing is structured, Google Cloud’s GPU pricing page, accessed on 2026-10-07, listed on-demand NVIDIA T4 GPUs at $0.35 per GPU-hour with discounted commitment columns. Don’t reuse that figure. It is an older, smaller GPU than what large-model training uses, and the page changes.
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Step 7: Benchmark the finalists with your own workload
The provider documentation reviewed here substantiates hardware, intended use, pricing mechanics and regional availability. It does not establish a neutral performance winner across providers. A benchmark you run is the only trustworthy tie-breaker.
- Freeze the test: same model and weights, framework and library versions, container image, precision, dataset sample, batch size or concurrency level, and sequence lengths.
- Match the hardware class: compare equivalent accelerator counts and memory, and note interconnect and CPU/RAM differences.
- Measure the right outputs: for training, tokens or samples per second, time to a target loss or step count, GPU utilization and scaling efficiency as you add nodes. For inference, throughput, time to first token, p50/p95/p99 end-to-end latency, and cost per unit of output at your target concurrency.
- Record operations too: provisioning time, failed or interrupted runs, restart effort, driver and framework setup friction, and the billed amount.
- Repeat: run enough times, ideally at different times of day, to see variability. A single run can mislead.
- Test at your real scale: a result on one node doesn’t always predict behavior on many nodes.
Step 8: Weigh the factors a benchmark can’t settle
Some criteria matter but can’t be ranked from public documentation. Check them against your own requirements instead of relying on a general claim.
- Security and compliance: certifications, data-residency options, private networking, encryption and access controls your organization requires.
- Support and account terms: support tiers, escalation paths for capacity problems, and contract or procurement constraints.
- Tooling and ecosystem: managed training and serving services, orchestration, monitoring, and how well your team already knows the platform.
- Portability: how tightly your code and data pipeline would bind you to one provider’s accelerators or services.
Quick decision guide by workload
| If your workload is… | Prioritize | Shortlist approach |
|---|---|---|
| Fine-tuning a small or mid-size model on one GPU | Accelerator memory, hourly cost, quota availability | Compare single-GPU configurations across providers; consider Spot with frequent checkpoints. |
| Large-model training across many GPUs | Intra-node link, inter-node fabric, cluster capacity for your dates | Start with eight-GPU nodes with documented high-speed networking, such as Azure ND H100 v5 or AWS P5; confirm cluster availability and commitments. |
| Online inference with latency targets | Memory for weights plus KV cache, batching behavior, regional proximity to users | Benchmark GPUs and, where your stack supports them, inference accelerators such as AWS Inferentia (Inf2). |
| Batch inference or offline processing | Cost per unit of output, tolerance for interruption | Compare discounted interruptible capacity against on-demand using measured throughput. |
| Regulated or region-bound data | Region availability and compliance coverage | Filter providers by required region first, then compare cost and performance. |
For a personalized pick, you need three inputs the public documentation can’t supply: your target region, your workload’s size and type, and whether you will pay on-demand, Spot/preemptible or committed rates. With those, repeat the steps above on two or three finalists, and re-run the comparison when prices, instance types or capacity change.
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