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The right GPU instance is the smallest configuration that keeps your model, runtime state and data pipeline within memory while meeting your latency or throughput target at an acceptable total cost. Start with workload type, model size, precision, sequence length and batch or concurrency; then size GPU memory, host RAM, CPU, storage, network and interconnect. Only after those constraints are clear should you compare GPU families such as A2, L4, A10G, H100, H200 or newer multi-GPU systems.
Use a representative benchmark before committing. Measure tokens or samples per second, p95 latency, utilization, memory headroom and cost per unit of work. Catalogs, quotas, prices and regional capacity change, so a benchmarked fit is more reliable than a permanent “best GPU.”
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What workload are you actually running?
Training and inference stress different parts of a GPU. Classify the job before looking at instance names, because a configuration that is excellent for pretraining can be wasteful for interactive serving.
Pretraining
Pretraining repeatedly processes huge datasets and usually scales across many GPUs. Tensor throughput, memory capacity, high-bandwidth memory and GPU-to-GPU communication dominate. Large models and mixture-of-experts systems may require high-memory, multi-GPU machines with fast interconnects.
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Fine-tuning
Fine-tuning normally uses fewer GPUs than pretraining, but optimizer states, activations and long sequences can still exceed the memory suggested by the model’s weight file. Parameter-efficient methods reduce trainable state, yet you still need room for activations, gradients, checkpoints and framework overhead.
Batch inference
Batch jobs trade latency for utilization. A GPU with more memory can hold a larger batch or several models, while a less expensive accelerator may deliver the lowest cost per item if it stays busy.
Interactive and real-time inference
Serving workloads care about first-token latency, tokens per second, concurrency and tail latency, commonly reported as p95. Quantization and smaller models can make an L4-class instance sufficient, but measure the actual model and request mix rather than assuming that a lower-precision checkpoint will behave identically.
Record these inputs
- Model parameter count and architecture, including mixture-of-experts routing.
- Weight, activation and optimizer precision (for example, FP32, FP16, BF16 or an integer format).
- Maximum sequence length, image resolution or other input dimensions.
- Training batch size, inference concurrency and expected request burst.
- Target samples per second, tokens per second, p95 latency or completion time.
- Whether the job can be interrupted and restarted from a checkpoint.
How much GPU memory and host RAM do I need?
GPU VRAM and system RAM are separate pools. A virtual machine can have ample host memory while the GPU runs out of VRAM; increasing RAM does not solve a CUDA out-of-memory error. Google Cloud documents this separation explicitly.
Count every category of GPU memory
- Weights: the model parameters at the precision used for execution.
- Activations: intermediate tensors retained for backpropagation during training, or temporary tensors during inference.
- Gradients and optimizer states: often the largest additional requirement in full-parameter training.
- KV cache: memory consumed by attention keys and values as context and concurrent sequences grow during autoregressive inference.
- Runtime workspace: memory reserved by CUDA, kernels, communication libraries and the framework.
- Safety headroom: space for variable sequence lengths, data-dependent peaks and checkpoint operations.
Do not size a machine to the nominal model file alone. Leave headroom for the highest real batch or concurrency you will permit; otherwise a rare long request or validation step can crash an otherwise healthy service.
Training versus inference memory
Inference usually avoids gradients and optimizer states, so the same model can fit on a smaller GPU. Training memory rises with batch size, sequence length and activation retention. Gradient checkpointing, sharding, lower precision and parameter-efficient fine-tuning can reduce the requirement, but each adds compute, communication or implementation complexity.
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Published capacity is not entirely available to your tensors. Drivers, the framework and communication buffers consume part of it. Treat a listed capacity as an upper bound, then verify free memory while running the actual model with production-shaped inputs.
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Which GPU families fit common workloads?
Families provide a starting point, not a guarantee. Match memory, tensor performance, interconnect and software support to the workload you recorded.
| Workload or tier | Representative families | Why they fit | Checks before choosing |
|---|---|---|---|
| Frontier pretraining and large MoE | Google Cloud A4X, A4 and A3; AWS P5 and P6-class systems | High-memory accelerators, high tensor throughput and multi-GPU scale for sharded training | Distributed framework support, topology, quota, checkpoint bandwidth and budget |
| Fine-tuning and smaller training | Google A2 40 GB or 80 GB; AWS G5 (A10G) and G6 (L4) | Suitable when model state, batch and sequence length fit on one or a few GPUs | Peak activation memory, optimizer state, host data-loader performance and required batch size |
| Interactive or cost-sensitive inference | Google G2 with L4; AWS G6 with L4 | Designed for cost-optimized inference and efficient serving of models that fit | p95 latency, concurrent KV-cache demand, quantization quality and regional capacity |
| Specialized AWS acceleration | Inf2 (Inferentia2) and Trn2 (Trainium2) | Can be attractive when the model and deployment stack support AWS Neuron | Operator coverage, compilation time, debugging workflow and migration effort |
| Video or visualization adjacency | Some G-series configurations | Provider documentation also lists video transcoding and virtual-workstation uses | These are separate use cases from model training; verify codec or graphics requirements |
Google’s current guidance places A4X, A4 and A3 at the large-model end, A2 for smaller-model training and single-host inference, and G2/L4 for cost-optimized inference. AWS lists G5, G6, P5, P5e/P5en, P6-B200, Inf2 and Trn2 as distinct options rather than one universal accelerator.
Is an H100, H200 or B200-class instance worth it?
A high-end accelerator is worth its premium when its memory, throughput or interconnect lets the job finish materially sooner or fit at all. It is not automatically cheaper per request or per training step.
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In AWS’s current EC2 accelerated-computing specifications, listed configurations provide 80 GiB per H100 GPU, 141 GiB per H200, 179 GiB per B200 and 268 GiB per B300. These are configuration figures from the provider’s documentation, not a cross-provider benchmark.
Google Cloud describes frontier and mixture-of-experts training systems with up to 279 GB of HBM3e per GPU and up to 1,800 GBps of NVLink bandwidth. Such systems target workloads where model partitioning and GPU-to-GPU traffic are central constraints.
When the premium can pay back
- The model cannot fit on a lower-memory accelerator without aggressive sharding or offload.
- Higher tensor throughput shortens a long training run enough to reduce total instance-hours.
- Faster interconnect reduces synchronization stalls in distributed training.
- Higher memory permits a useful batch or concurrency level that improves utilization.
When it is likely wasteful
- A small model leaves most of the accelerator idle.
- Your bottleneck is data loading, preprocessing, storage or network transfer.
- The framework or kernels do not support the accelerator efficiently.
- Interactive traffic is sparse and a lower-cost GPU can meet p95 latency.
Compare cost per completed training step, generated token or successful request, not hourly price alone. A less expensive GPU that takes twice as long can cost more, while a premium GPU running at low utilization can be equally inefficient.
Should I use one GPU or a multi-GPU instance?
Use one GPU for development, small fine-tunes and inference that fits with headroom. Move to multiple GPUs when memory capacity, throughput or concurrency requires it, and confirm that your software can exploit the topology.
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Single-GPU advantages
- Simpler installation, debugging and checkpoint recovery.
- No inter-GPU synchronization overhead.
- More regional availability and easier quota management.
- Lower blast radius when a job fails.
Multi-GPU requirements
Sharded training needs a distributed strategy such as data, tensor or pipeline parallelism. Communication-heavy workloads benefit from NVLink or an equivalent high-bandwidth fabric; PCIe-only layouts can become a bottleneck. Serving can also use multiple GPUs for replicas, but replication increases memory consumption, while model parallelism increases operational complexity.
Check GPU count, peer-to-peer topology, interconnect bandwidth, network interface speed and placement across hosts. A nominally identical GPU count can deliver different results if one instance keeps traffic on a fast local fabric and another crosses the host network.
What CPU, RAM, storage and network configuration should accompany the GPU?
The accelerator is only one part of the pipeline. A fast GPU that waits for data or checkpoints can cost more while doing less useful work.
CPU and data loading
Profile preprocessing, tokenization, decompression and augmentation. Allocate enough vCPUs for the number of workers that keep the GPU fed, but avoid paying for idle cores. Pinpoint whether CPU utilization, input-queue depth or GPU utilization drops during stalls.
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Host RAM
System memory holds datasets, prefetch buffers, operating-system cache and distributed-training state. It cannot substitute for VRAM, but too little RAM can cause swapping and starve the data loader.
Storage
Use storage that sustains the read rate of your dataset and the write rate of checkpoints. Account for local scratch space, persistent volumes, checkpoint retention and restart downloads. A training run that repeatedly waits on network storage may erase the benefit of a faster GPU.
Network
Distributed training requires predictable bandwidth and low latency between workers. Inference services need enough ingress and egress capacity for request payloads and streamed responses. Include transfer charges and the time required to restore checkpoints when comparing regions.
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How do Google Cloud, AWS and Azure GPU catalogs differ?
Provider naming and accelerator choices are not interchangeable, so compare the complete instance specification rather than only the GPU model.
Google Cloud
Google’s guidance maps A4X/A4/A3 to large-model training, A2 to smaller training and single-host inference, and G2/L4 to cost-optimized inference. Its documentation also separates GPU memory from host RAM and describes high-bandwidth systems for frontier workloads.
AWS
AWS offers GPU families including G5 with A10G, G6 with L4, P5 with H100, P5e/P5en with H200 and P6-B200, alongside Inferentia2 and Trainium2. AWS lists 32 GiB per Inferentia2 accelerator and 512 GiB per Trainium2 accelerator in its current specifications. Those accelerators require a compatible AWS Neuron toolchain; framework support should be proven with your model before migration.
Azure
Microsoft’s guidance associates ND-series machines with training and NC or ND with inference. Azure Data Science VM images bundle common machine-learning tools plus NVIDIA drivers, CUDA and cuDNN, which can reduce setup work; verify the image version and framework compatibility you need.
Availability and quota
Check the exact region, zone, quota and machine shape before designing around a family. Capacity can differ by region, and a quota approval does not guarantee immediate stock. Keep a tested fallback family or region when the workload is time-sensitive.
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Are Spot GPU instances safe for training?
Spot capacity is practical for interruptible jobs, not for a process that cannot recover. Google Cloud’s current guidance says Spot instances can be 60–91% cheaper than on-demand for most machine types and GPUs, but they may be reclaimed with 30 seconds’ notice.
Make interruption survivable
- Save checkpoints to durable storage at a cadence tied to the cost of lost work.
- Store optimizer, scheduler, random-state and data-shard state, not just model weights.
- Automate detection of termination notices and trigger a final checkpoint immediately.
- Use an idempotent startup script that recreates the environment and resumes from the newest valid checkpoint.
- Keep a tested on-demand fallback or queue so repeated reclamation does not halt the project.
- Track effective cost including discarded work, restart time and storage operations.
Spot is often a good fit for distributed hyperparameter sweeps, batch inference and fault-tolerant training. It is risky for latency-sensitive serving unless you have replicas and an automatic capacity replacement path.
How do I benchmark GPU instances before committing?
Benchmark the complete job, not a synthetic kernel or a single short prompt. Use the same model version, precision, data preprocessing, sequence lengths, batch or concurrency and output limits you expect in production.
- Prepare a representative slice. Include long and short sequences, typical images or documents, realistic class balance and the largest supported request.
- Warm up the system. Exclude image pull, compilation and first-request initialization from steady-state measurements, but record those startup times separately for autoscaling decisions.
- Measure throughput. Record samples per second for training or tokens per second for generation, along with step time and scaling efficiency.
- Measure latency. For serving, capture time to first token, completion time and p50, p95 and p99 latency at the intended concurrency.
- Observe utilization and memory. Track GPU utilization, VRAM high-water mark, host CPU, RAM, input-queue depth and network throughput.
- Test failure and restart. For Spot or distributed jobs, terminate a worker and verify checkpoint integrity and recovery time.
- Calculate unit cost. Combine GPU, vCPU, RAM, storage, transfer, checkpoint, idle and restart costs; report cost per training step, sample or generated token.
- Repeat across candidates. Run enough iterations to smooth startup effects and compare the same success criteria, not just the fastest single run.
What commonly goes wrong, and how can I fix it?
CUDA out-of-memory despite abundant system RAM
Reduce batch size or sequence length, enable an appropriate lower-precision mode, use gradient checkpointing or sharding, and leave more VRAM headroom. Moving data to host RAM can avoid a crash only if the framework supports offload; it usually adds latency.
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Inspect data-loader wait time, CPU preprocessing, storage throughput and synchronization barriers. Increase worker capacity or prefetching only after confirming the bottleneck, and avoid increasing batch size beyond the memory and latency target.
Multi-GPU scaling is poor
Check whether communication crosses a slow link, whether the batch is large enough to amortize synchronization, and whether the distributed backend is using the intended topology. A smaller number of GPUs with a faster fabric can beat a larger, loosely connected group.
Inference latency spikes under load
Measure queueing and KV-cache growth, not only kernel time. Cap concurrency or context length, reserve memory headroom, batch requests deliberately and test p95 rather than relying on an average.
The chosen accelerator lacks a required operation
Run the exact container and framework build on the target instance before purchase. Specialized accelerators can be economical only when every critical operator, compiler path and monitoring tool is supported.
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What is the fastest decision process?
- Classify the job as pretraining, fine-tuning, batch inference, interactive inference or real-time serving.
- Estimate weights, activations, optimizer state, KV cache and workspace at the target precision and batch or concurrency.
- Select a single-GPU or multi-GPU topology that leaves memory headroom and supports the required parallelism.
- Shortlist families by memory and throughput: A4X/A4/A3 or P5/P6 for frontier scale; A2, G5 or G6 for smaller training; G2/L4 or G6 for cost-sensitive inference; Inf2 or Trn2 only with validated Neuron support.
- Verify CPU, RAM, storage, network, drivers, CUDA, framework containers, quotas and regional capacity.
- Compare on-demand, Spot and commitment options using total cost and interruption tolerance.
- Benchmark representative data and retain the smallest configuration that meets the measured target with operational headroom.
Frequently Asked Questions
Can I run a large language model on a GPU with less VRAM than its parameter count suggests?
Sometimes, but only with techniques such as quantization, CPU or disk offload, tensor parallelism, or aggressive context limits. These approaches trade memory for latency, throughput, quality or operational complexity, so validate the exact serving stack rather than relying on a parameter-count rule.
Does quantization always make inference cheaper?
No. Lower-precision weights can reduce memory and permit a less expensive instance, but conversion overhead, unsupported kernels or extra dequantization work may lower throughput. Compare cost per completed request at the target quality and concurrency.
How often should a preemptible training job checkpoint?
Choose an interval shorter than the amount of work you can afford to lose when a reclaim occurs, then test the actual checkpoint write time. Very frequent checkpoints can throttle storage and training, while infrequent ones increase restart waste; tune the interval from measured step time and recovery cost.
What should I keep portable between cloud providers?
Package the model, tokenizer, dependencies and launch configuration in a reproducible container, and store checkpoints in a format your framework can restore without provider-specific paths. Keep a provider-neutral benchmark script so a fallback instance can be qualified quickly when quotas or regional capacity change.
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