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Provider catalogs are useful for narrowing candidates, but they do not establish a universal best instance. Run a representative job on the finalists and compare time to train, useful inference throughput or latency, failure behavior and total cost per completed job. Confirm live capacity, regional pricing and exact machine specifications immediately before deployment.
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1. Describe the workload before looking at instance names
Instance families are organized around different bottlenecks. A training run that keeps several copies of optimizer state in memory has different requirements from a small online inference service. Write down the following before opening a cloud catalog:
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- Task: pretraining, fine-tuning, evaluation, batch inference or online inference.
- Model and data: parameter count, input shape or sequence length, dataset size and whether data is transformed on the host.
- Peak memory: weights, activations, optimizer state, batches, temporary tensors and, for generative inference, the key-value (KV) cache.
- Performance target: time to train, samples or tokens per second, requests per second, or a latency percentile such as p95.
- Execution pattern: one long job, many short jobs, an always-on endpoint or a queue of interruptible work.
- Software: framework, accelerator support, container image, driver and runtime requirements, orchestration and quota needs.
- Reliability: whether the job can checkpoint and resume after a machine is reclaimed or fails.
These notes turn an open-ended catalog search into a set of constraints. If a candidate fails a hard constraint—usually per-GPU memory, software support or required latency—there is no benefit in comparing its hourly price.
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Training and fine-tuning
Training usually stresses memory, sustained GPU throughput, data-loading capacity and, for distributed jobs, communication between accelerators. Full training may need memory for weights, gradients, optimizer state and activations. Fine-tuning can require less compute, but sequence length, batch size and optimizer choice can still make memory the limiting factor. Record the largest batch and input size you must support, not only the average case.
Batch inference
Batch inference often values throughput and low cost per completed item over interactive latency. It is a good candidate for queue-based scheduling, checkpointing and a reclaimable capacity model if work can be retried. Storage throughput can matter as much as the GPU when the pipeline repeatedly reads large files or writes predictions.
Online inference
An endpoint has a latency and availability contract. Reserve enough memory for the model, runtime overhead and KV cache, then leave headroom for concurrent requests. A smaller single-GPU machine may be easier to operate than a multi-GPU machine if the model fits and the measured latency target is met. Avoid selecting a large instance solely because it has a higher theoretical accelerator count.
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GPU memory is not the same as the host machine’s RAM. Google Cloud describes GPU memory as memory on the GPU device, separate from instance memory and designed for high-bandwidth graphics and compute workloads. A machine can have abundant system RAM while still failing because one accelerator runs out of device memory.
Estimate the peak footprint of weights, gradients, optimizer state, activations, input batches, framework workspace and inference caches. Add headroom for compilation, variable-length inputs, larger production batches and checkpoint operations. The estimate must be made per GPU when a framework places a model or a batch on individual devices.
Per-GPU memory versus aggregate memory
Adding the capacities of several GPUs does not automatically create one shared pool. A distributed data-parallel job normally keeps a copy of relevant state on each device. Model or pipeline parallelism can divide a model across devices, but it requires framework support, an appropriate topology and communication that does not erase the speed benefit. Confirm how your chosen software uses multiple GPUs before treating aggregate capacity as usable model memory.
| Example configuration | Accelerator layout | GPU memory | What the figure means |
|---|---|---|---|
Google Cloud A3 a3-highgpu-1g |
One H100 | 80 GB HBM3 | One device must hold the workload’s per-device footprint. |
Google Cloud A3 a3-highgpu-8g |
Eight H100 GPUs | 640 GB total | Total capacity is eight devices; it is not automatically a single 640 GB address space. |
AWS SageMaker P5 ml.p5.48xlarge |
Eight H100 GPUs | 80 GB per GPU; 640 GB aggregate | The per-GPU limit and the multi-GPU topology both affect what fits. |
AWS SageMaker P4d ml.p4d.24xlarge |
Eight A100 GPUs | 40 GB per GPU; 320 GB aggregate | A model that fits on an 80 GB device may require sharding or a different configuration here. |
Google Cloud G2 g2-standard-4 |
One L4 | 24 GB GDDR6 | One L4 paired with four vCPUs; larger G2 sizes use different host resources. |
The values above are catalog specifications recorded from provider pages accessed in 2026, not independent performance measurements. Check the live machine catalog for the region and zone you intend to use.
3. Match accelerator generation, count and interconnect
Once memory is sufficient, compare the accelerator generation and the number of GPUs. Newer or larger accelerators can reduce training time, but the useful comparison is measured throughput or latency for your model and framework. A theoretical specification or a provider’s maximum claim is not a substitute for a representative run.
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When one GPU is the better choice
- The model and production batch fit comfortably on one device.
- The target latency or throughput is met without distributing work.
- The job is small enough that multi-GPU communication would add overhead.
- Simpler scheduling, debugging and checkpoint handling are valuable.
When multiple GPUs are justified
- The model cannot fit on one device and supported model or pipeline parallelism is available.
- A measured training run scales well enough to reduce time to a business deadline.
- Large batches or concurrent inference requests provide enough parallel work to keep every device busy.
- The instance provides the required peer-to-peer links and network bandwidth.
For example, AWS lists 3,200 Gbps EFAv2 bandwidth and NVSwitch peer-to-peer connectivity for the SageMaker P5 configuration cited above. Those properties are relevant to distributed workloads; they do not guarantee a particular speedup. Measure scaling from one to two, four and eight GPUs where possible, and include synchronization time in the result.
Do not confuse GPU count with usable scaling
Distributed training can spend time synchronizing gradients, moving activations or waiting for the slowest worker. A workload with small batches, frequent synchronization or an input pipeline that cannot feed all devices may gain little from additional GPUs. Record GPU utilization and communication time during a pilot rather than assuming linear scaling.
4. Size the host CPU, RAM, storage and network
A GPU instance is a complete machine configuration. The accelerator sits alongside vCPUs, system memory, storage, network interfaces and, on multi-GPU systems, an interconnect. An undersized host can leave an expensive GPU idle.
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CPU and system memory
Preprocessing, tokenization, image decoding, augmentation, data decompression and checkpoint serialization run partly or entirely on the host. Increase vCPUs when data-loader workers are saturated or preprocessing delays the next GPU batch. Increase system RAM for large in-memory caches, multiple workers, large batches staged before transfer or checkpoints assembled on the host. GPU memory and system memory solve different problems; size both independently.
Storage capacity and throughput
Capacity must cover the operating environment, container layers, dataset or cache, checkpoints, logs and temporary files. Throughput and I/O operations per second determine whether the loader can sustain the accelerator. Test the actual access pattern—sequential shards, many small files, random samples or repeated checkpoint writes—instead of relying only on advertised capacity. Decide whether local ephemeral disks are acceptable; durable attached storage may be necessary for recovery, but it can add cost and latency.
Network and interconnect
Network bandwidth affects dataset reads, checkpoint uploads, distributed communication and serving traffic. Multi-node training needs a low-latency, high-bandwidth path supported by the framework and cluster configuration. Within a machine, GPU-to-GPU links such as NVSwitch can be more important than the external network for model or pipeline parallelism. Check the exact topology, not just the family’s maximum network number.
AWS describes the G4dn family as using NVIDIA T4 GPUs and custom Intel Cascade Lake CPUs, optimized for inference and small-scale training. The family page lists up to eight GPUs, 96 vCPUs, 100 Gbps networking and 1.8 TB of local NVMe storage. Those are family maxima: an individual G4dn size may include fewer GPUs and less CPU, network or storage. Always inspect the exact size.
5. Verify software, drivers and service limits
Before provisioning, confirm that the selected image and orchestration system support the accelerator. Check the provider’s current compatibility guidance for:
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- Framework and version support.
- GPU driver, CUDA or other accelerator runtime requirements.
- Container base image and kernel expectations.
- Distributed-training libraries and communication backends.
- Persistent-volume and object-storage access.
- Quota, regional capacity and maximum GPU allocation.
- Managed training, batch or endpoint services you plan to use.
Do not infer compatibility from the GPU name alone. A machine can be technically available while the required driver, image, quota or managed workflow is unavailable in your target region. Pin the image and runtime for reproducibility, and keep a tested fallback instance family if the preferred capacity disappears.
6. Choose an availability and interruption model
Interactive development, customer-facing inference and deadline-bound training normally need dependable on-demand or committed capacity. Batch work that can restart has more options. Google Cloud describes Spot VMs as surplus capacity that can be reclaimed, with 30 seconds’ notice, and identifies fault-tolerant, checkpointed batch machine-learning jobs as a suitable use.
Use reclaimable capacity when the job is designed for it
Write checkpoints to durable storage at a cadence that limits lost work. Make startup repeatable: the job should restore the latest checkpoint, rebuild its data-loader state as needed and continue without manual intervention. Add retry limits and alerting so a capacity shortage does not create an unnoticed loop. Spot pricing and availability are dynamic, so evaluate both expected savings and the cost of interruptions.
Prefer stable capacity for latency-sensitive services
An endpoint cannot assume that a replacement machine will appear within a recovery window. Account for warm-up time, model download, health checks and traffic draining. If redundancy is required, model the cost of more than one instance and verify that the required GPU capacity exists in the selected zones.
7. Compare total cost, not an hourly GPU figure
Hourly GPU price is only one line in the bill. Include the machine type, vCPU and RAM charges, disks, object storage, data transfer, managed-service fees, orchestration overhead and idle time. For training, calculate cost per completed run. For inference, calculate cost per million requests, images, tokens or other useful unit.
Google’s pricing documentation notes that GPU prices are regional and that each GPU adds to the cost of the instance in addition to the machine type. Its pricing calculator includes GPU and machine configuration costs. Use the calculator for the exact region and configuration rather than copying a price from another zone or date.
| Cost item | Questions to answer |
|---|---|
| Accelerator and machine | Which GPU model, count, vCPU and RAM are billed, and under which billing mode? |
| Storage | Are local disks ephemeral? What are the capacity, throughput and persistent-storage charges? |
| Data movement | Will datasets, checkpoints or predictions cross zones, regions or provider boundaries? |
| Managed services | Are training control planes, endpoint management, logging or orchestration billed separately? |
| Utilization | How many hours are productive, and how much time is spent provisioning, downloading data or idle? |
| Interruption and retries | How much recomputation or queue time results from reclaimable capacity? |
Any quoted hourly figure must state provider, region, exact machine type, date, billing model and included components. No universal cross-provider price ranking follows from the specifications above; prices and capacity change by location and time.
8. Benchmark the actual job before committing
A short, representative benchmark is more valuable than a long list of theoretical specifications. Use the same model revision, input shape, precision, batch policy, software image and data path on each finalist.
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- Prepare a repeatable workload. Use a representative dataset slice large enough to expose data-loading behavior, and run a warm-up before recording results.
- Measure the target outcome. Record training time to a defined checkpoint or quality level, inference throughput, and p50/p95/p99 latency where relevant.
- Record resource behavior. Capture GPU utilization, peak GPU memory, host CPU and RAM usage, storage throughput, network traffic and communication time.
- Test failure and restart. Kill a worker or simulate reclamation if the workload may use Spot capacity. Confirm checkpoint recovery and quantify lost work.
- Calculate effective cost. Multiply the measured runtime by all applicable charges, then include retries, storage, transfer and idle periods.
- Repeat at production scale. A configuration that looks good for one request or one training epoch may behave differently with the intended concurrency or dataset.
Keep the benchmark results with the image digest, driver/runtime versions, region, machine size and date. Provider pages are living documents, so recheck specifications and capacity when the deployment changes.
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The following are selection patterns, not performance recommendations. The correct choice still depends on the measured job.
Large, memory-intensive training
Start with an accelerator offering enough memory per GPU for the model state and planned batch. If one device is insufficient, shortlist multi-GPU systems with a suitable peer-to-peer topology and distributed software support. Compare a smaller number of large-memory devices with a larger number of smaller-memory devices; the latter may require more aggressive sharding and communication.
Small-scale training or fine-tuning
Begin with a single GPU that leaves memory headroom and a host capable of feeding it. AWS positions its G4dn/T4 family for inference and small-scale training. Treat that as provider guidance for a shortlist, then verify your own throughput and memory use. Move to a larger accelerator only when the measured time-to-train or memory limit justifies it.
Cost-sensitive batch inference
Prioritize throughput per dollar, durable checkpoints and queue-based retries. A G2 configuration with an L4 can be a candidate for cost-optimized inference; Google lists g2-standard-4 with one 24 GB L4 and four vCPUs. Check whether the host can preprocess and feed the GPU, and test Spot capacity if the queue tolerates interruption.
Interactive or online inference
Choose the smallest configuration that meets measured latency at peak concurrency with memory headroom. Include model-load time, autoscaling behavior, health checks and redundancy in the design. Stable capacity is usually more important than a nominally lower interruptible rate when missed requests have a direct impact.
10. Common mistakes and how to correct them
Choosing by GPU name alone
Symptom: the selected GPU is fast on paper but utilization remains low. Correction: inspect CPU preprocessing, storage reads, network waits and synchronization time; resize the host or simplify the topology.
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Symptom: a model reported as smaller than total memory still fails allocation. Correction: calculate the per-device footprint and verify supported model-parallel placement.
Using a family maximum as an exact specification
Symptom: the provisioned size has fewer GPUs, less NVMe or lower network bandwidth than expected. Correction: read the row for the exact instance size, not the family headline.
Comparing prices from different regions
Symptom: the estimate changes at deployment or the machine cannot be provisioned. Correction: align provider, region, zone, machine type, billing mode and date, then confirm live capacity.
Putting an interruptible job on Spot without checkpoints
Symptom: a reclaim causes hours of lost work. Correction: checkpoint to durable storage, automate resume and include retry and alerting logic before using reclaimable capacity.
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Ignoring quotas and software readiness
Symptom: the desired GPU appears in the catalog but provisioning or startup fails. Correction: request quota early, validate image and runtime compatibility, and maintain a tested fallback.
11. A concise decision checklist
- Have you identified training, fine-tuning, batch inference or online inference?
- Do you know peak model, activation, optimizer, batch and cache memory?
- Does each GPU have enough memory with headroom?
- Will the framework use multiple GPUs as intended, and does the topology support it?
- Are vCPUs, system RAM, storage throughput, capacity and network sufficient?
- Are the driver, runtime, container, framework, quota and managed services supported?
- Can the job tolerate a 30-second reclaim notice and automatic restart if using Spot?
- Have you included GPU, machine, storage, transfer, service, retry and idle costs?
- Did you benchmark the exact model and record useful work per dollar?
- Did you verify current regional price and capacity immediately before launch?
Frequently Asked Questions
Is more GPU memory always better for machine learning?
No. Memory is a hard fit requirement, but once the model and batch fit, extra capacity may add cost without improving throughput. Compare measured performance and leave practical headroom rather than buying the largest device by default.
Can I combine the memory of several GPUs for one model?
Only when the framework and model-parallel strategy explicitly support distributing the model. Multi-GPU memory is not automatically one shared pool, and communication overhead can reduce the benefit.
Should I use Spot capacity for training?
Use it for checkpointed, restartable batch work that can tolerate reclamation. Interactive, latency-sensitive or deadline-bound jobs generally need more dependable capacity.
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Use effective cost for a completed training run or a defined volume of useful inference, alongside quality, latency and failure behavior. Hourly price alone omits utilization and retry costs.
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.
