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Start by defining the workload and what success means
“AI workload” covers tasks with different bottlenecks. Training, fine-tuning, batch inference, and interactive serving can favor different hardware and configurations. Before comparing products, write down what you need the system to do and the conditions under which it must do it.
- Task and model: Identify the training or inference task, model, and relevant input and output sizes.
- Numerical precision and quality: Record the precision you intend to use and any minimum quality or accuracy requirement. Faster results are not useful if they fail that requirement.
- Concurrency and batch size: Specify the expected number of simultaneous requests or the batch size. These affect both throughput and memory use.
- Latency or completion target: For interactive serving, set a latency target; for training or batch work, decide how quickly the job must finish.
- Scale: Note whether one accelerator is sufficient or the workload must span several accelerators or hosts.
- Operational constraints: Include deployment region, software requirements, capacity needs, and any limits on data movement or service terms.
Choose the metric that reflects the actual goal: end-to-end training time, throughput while meeting a latency target, or cost per useful output. A single peak-throughput figure cannot answer all three.
Check memory fit before comparing compute
First establish whether the model weights, runtime state, and active working data fit in the accelerator’s memory. Host RAM is separate: an instance’s system memory does not become GPU memory just because both appear in the same specification table. If a workload does not fit, it may need a different configuration or a different execution strategy; compare candidates only after accounting for that requirement.
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Capacity is only part of the memory question. Memory bandwidth affects how quickly data can reach the accelerator, and communication between accelerators matters when a job is split across devices. Google Cloud’s AI accelerator performance and benchmarking documentation describes the roofline relationship: “The slanted roof (memory bound): Attainable Performance = Peak Memory Bandwidth × Operational Intensity.” In its examples, autoregressive decoding at batch size one is memory-bound, while GEMMs and large-batch convolutional neural networks are compute-bound. The lesson is to identify the bottleneck for your own workload rather than assume that a higher compute rating will make it faster.
Compare the complete instance, not just the accelerator
A cloud instance combines an accelerator with host CPUs, host memory, storage, networking, and a service configuration. These resources can constrain data loading, checkpointing, distributed training, or serving even when the accelerator itself is capable. When comparing offerings, record these alongside GPU count and GPU memory.
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| Comparison axis | What to check | Why it affects the decision |
|---|---|---|
| Workload | Training, fine-tuning, batch inference, interactive serving, or another defined task | Different tasks stress compute, memory, and communication differently. |
| Memory | Accelerator memory capacity and bandwidth; host RAM separately | Capacity determines whether the model and working set fit; bandwidth influences data movement. |
| Compute | Supported precision and measured workload throughput | Peak theoretical arithmetic is not a guarantee of application performance. |
| Scaling | Accelerator count, interconnect, host networking, and distributed software | Multi-accelerator performance depends on communication and scale efficiency. |
| Software | Framework, kernels, compiler, drivers, libraries, and model support | The workload must run correctly and efficiently on the available stack. |
| Instance | vCPU, host RAM, local or attached storage, network, and accelerator configuration | CPU input pipelines, checkpointing, data access, or networking can bottleneck the system. |
| Service economics | Region, billing terms, utilization, storage, and data-transfer costs | A chip-only price omits costs that contribute to the deployed workload. |
| Evidence quality | Benchmark version, model, precision, quality constraints, scale, and submitter | Results are meaningful to your choice only when their conditions and metric are relevant and disclosed. |
Use this as a filter before running tests: eliminate instances that cannot meet the memory, software, scale, or deployment requirements. Then compare only configurations that are plausible candidates.
Use cloud catalog specifications as examples, not performance rankings
Provider catalogs help identify configurations to investigate, but their stated use cases do not establish which option is fastest or least expensive for your workload. The following are configurations described in Google Cloud’s GPU machine types | Compute Engine documentation and AWS’s Accelerated computing | Amazon EC2 instance types documentation. They are catalog examples, not an independent cross-provider benchmark; verify the current configuration, region, and capacity before choosing.
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| Provider catalog example | Documented accelerator configuration | Documented context and qualification |
|---|---|---|
| Google Cloud A4X | GB200 Grace Blackwell Superchips; the listed a4x-highgpu-4g system has four GPUs and 744 GB of GPU memory. |
Google Cloud describes A4X for foundation-model training and serving. Check current regional availability and instance details in the provider catalog. |
| Google Cloud A3 Ultra | Eight H200 GPUs and 1,128 GB of aggregate GPU memory in the listed instance. | Google Cloud documentation notes a capacity reservation, Spot, Flex-start, or resize-request requirement. Confirm the applicable capacity path and availability. |
| Google Cloud A3 and A2 | A3 H100 configurations and A2 A100 instances are listed. | The cited catalog description identifies these families; it does not establish a comparable workload-performance result for them. |
| Google Cloud G4 and G2 | G4 with RTX PRO 6000; G2 with L4 GPUs. | Google Cloud describes G2 with L4 for cost-optimized inference. That provider positioning is not proof it is cheapest for a particular workload. |
| AWS EC2 G6 | L4 GPU configurations include single-GPU instances with 24 GB of GPU memory and multi-GPU configurations up to eight L4 GPUs. | AWS describes G6 for graphics-intensive applications and machine-learning inference. Its catalog also lists vCPU, host memory, network bandwidth, and EBS bandwidth; compare the full configuration. |
| AWS EC2 G7 | RTX PRO 4500 Blackwell Server Edition GPUs. | AWS describes this as a newer instance family. The cited catalog information does not establish a matched performance comparison with other providers. |
Google Cloud also describes A-series instances for AI and machine learning, including foundation-model pretraining and fine-tuning at larger scales. Treat these descriptions, like the G2 and G6 use cases, as provider guidance about intended uses—not independent evidence of a universal winner.
Benchmark candidates under the same conditions
Once you have narrowed the field, run a test that resembles the actual deployment. Keep the conditions consistent across candidates so a difference in batch size, model, precision, or quality target does not masquerade as a hardware advantage.
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- Match the model and task. Use the intended model and a representative training or inference workload.
- Fix the workload conditions. Align precision, batch size or concurrency, input and output lengths, and any quality requirement.
- Include the relevant scale. Test the number of accelerators and hosts you expect to use, with the software and communication setup intended for deployment.
- Measure the target metric. Record end-to-end job time, or throughput at the required latency and quality, as appropriate. Include setup or data movement when it is part of the real workflow.
- Compare the complete configuration. Record instance resources, software stack, benchmark version, and the conditions that produced each result.
MLPerf describes its evaluations as prescribed-condition benchmarks for training and inference across hardware, software, and services. Its suite evolves over time, so note the benchmark round and workload rather than treating an MLPerf result as timeless or directly interchangeable with a different test. NVIDIA’s MLPerf page reports NVIDIA-submitted v6 results; interpret those as NVIDIA’s account of its submissions and retain the specific entry, system scale, workload, and metric. A benchmark result does not establish that one vendor is universally faster than every alternative.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Calculate cloud cost for the actual deployment
A useful cost comparison needs the whole workload, not a GPU-only rate. Include the accelerator instance, host resources, storage, networking or data transfer, expected utilization, runtime, and applicable billing commitment. Use current provider pricing for the intended region and billing model, and record the date because prices and availability can vary. The catalog and benchmark facts above do not establish comparable prices or capacity across vendors and regions, so they are not enough to name a cheapest option.
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For a fair comparison, estimate the cost to complete the same useful work under the same quality and latency constraints. A lower hourly instance price may not mean lower cost per completed job if the workload takes longer or uses more resources; a throughput advantage matters only if it holds under your measured conditions.
Quick Recap
Make the decision in order
- Define the workload and target. State the model, task, quality, latency or completion goal, concurrency, and scale.
- Apply feasibility filters. Check accelerator memory, software support, deployment region, and capacity requirements before considering performance.
- Compare complete candidate instances. Include host, storage, networking, accelerator count, interconnect, and service constraints.
- Benchmark representative candidates. Keep conditions aligned and use the metric that matches the operational goal.
- Evaluate economics and availability. Price the complete instance for expected runtime and utilization using current regional terms, then confirm that the required capacity can be obtained.
- Choose on evidence that matches the decision. If the evidence differs in model, precision, scale, or metric, treat it as directional rather than a direct comparison.
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.
