The GPU infrastructure landscape for ML training has shifted dramatically over the past year. Supply constraints are easing for some accelerator families while tightening for others, cloud providers have introduced new instance types and pricing models, and the economics of on-premises builds have changed with the latest generation of hardware. If you are making a GPU infrastructure decision in 2026, the calculus is different from even twelve months ago.

This is a practical comparison based on infrastructure we have built and operated for ML teams running training workloads from single-node experiments to multi-node distributed training.

The Current AWS GPU Landscape

AWS offers several GPU and accelerator instance families relevant to ML training. Understanding their positioning is essential for cost-effective architecture.

P5 Instances (H100)

The P5 family provides NVIDIA H100 GPUs with 80 GB HBM3 memory per GPU, up to 8 GPUs per instance, and 3200 Gbps EFA networking for distributed training. P5 instances are the default choice for large-scale training workloads. Availability has improved significantly since mid-2025, but on-demand capacity in popular regions still requires planning. Capacity reservations are available for committed workloads.

P5 on-demand pricing runs approximately $98 per hour for a p5.48xlarge (8x H100). That is around $70,000 per month for a single instance running continuously — a number that makes on-premises economics look attractive at first glance.

P5e and P5en Instances (H200)

The newer P5e family features H200 GPUs with 141 GB HBM3e memory, a substantial increase over the H100’s 80 GB. The extra memory is particularly valuable for training large language models where activation memory is the bottleneck. P5en instances add enhanced networking for tighter distributed training coupling. Availability is still limited but expanding steadily across regions.

P4d Instances (A100)

P4d instances with 8x A100 40 GB GPUs remain a workhorse for training workloads that do not require the latest generation. On-demand pricing around $32 per hour makes them significantly more accessible than P5. For many training jobs — fine-tuning, medium-scale pretraining, computer vision models — A100s deliver excellent price-performance.

Trn1 and Trn2 Instances (Trainium)

AWS Trainium chips are the wildcard. Trn1 instances offer up to 16 Trainium chips per instance at roughly 50 percent of the cost of equivalent P5 capacity. Trn2 instances, based on the second-generation Trainium2 chip, further improve performance and memory. The catch is software compatibility: training on Trainium requires the Neuron SDK, which supports PyTorch and JAX but not every model architecture and optimization technique out of the box. If your workloads fit the supported matrix, Trn instances are the most cost-effective training option on AWS by a wide margin.

Inf2 Instances (Inferentia2)

Inf2 is purpose-built for inference, not training, but worth mentioning because ML teams often need both. Running inference on Inf2 instead of general-purpose GPUs can reduce serving costs by 40 to 70 percent, freeing budget for training infrastructure.

The On-Premises Case

On-premises GPU infrastructure makes a compelling argument when utilization is consistently high.

Hardware Economics

An 8x H100 SXM server (DGX H100 or equivalent) costs approximately $250,000 to $350,000 depending on configuration and vendor. Add networking (InfiniBand for multi-node), rack infrastructure, power, cooling, and a three-year support contract, and the all-in cost is roughly $400,000 to $500,000 per server.

Amortized over three years, that is approximately $11,000 to $14,000 per month — compared to $70,000 per month for the equivalent P5 on-demand instance on AWS. Even with reserved pricing, cloud costs for continuous utilization are three to four times higher than on-premises over a three-year horizon.

The math is unambiguous: if you can keep a GPU server utilized at 70 percent or higher for three years, on-premises wins on raw cost.

Where On-Prem Falls Apart

The raw cost comparison hides significant operational complexity.

Lead times. H100 servers are shipping with more predictable timelines than during the 2023-2024 shortage, but procurement still takes 8 to 16 weeks. B100 and GB200-based systems have longer queues. Cloud instances are available in minutes.

Scaling. If a research breakthrough requires 4x your current compute for a three-month experiment, on-premises cannot respond. Cloud can.

Utilization risk. The three-year amortization assumes sustained high utilization. If your training workload profile changes — model architecture shifts, a project is cancelled, priorities pivot — on-premises hardware becomes a stranded asset. Cloud spend scales down with demand.

Operational overhead. On-premises GPU clusters require specialized staff for hardware maintenance, driver management, InfiniBand fabric troubleshooting, power and cooling monitoring, and security patching. This is a full-time role for every 50 to 100 GPUs. Factor in the fully loaded cost of that headcount.

Data pipeline integration. Most ML teams already have data stored in S3, experiment tracking in cloud-native tools, and CI/CD in the cloud. On-premises training introduces data movement latency and pipeline complexity that is easy to underestimate.

Cost Modeling for Different Scales

The right infrastructure choice depends heavily on the scale and profile of your training workloads.

Small Scale: Experimentation and Fine-Tuning (1-8 GPUs)

For teams running experiments, hyperparameter sweeps, and fine-tuning on a few GPUs, cloud is almost always the right choice. The workload is bursty, utilization per GPU is variable, and the operational overhead of on-premises hardware is disproportionate to the compute need.

Recommended approach: P4d or P5 spot instances for experiments. Spot pricing for P4d runs 60 to 70 percent below on-demand, and training jobs can be checkpointed and resumed on interruption. Use SageMaker managed training jobs or a lightweight orchestrator like Ray on EKS to handle spot interruptions automatically.

Monthly cost estimate: 3,000 to 15,000 EUR depending on hours and instance type.

Medium Scale: Regular Training Runs (8-64 GPUs)

This is the awkward middle ground where the decision is hardest. Workloads are substantial enough that cloud costs are significant, but not large enough to justify a dedicated on-premises cluster with the associated staffing.

Recommended approach: Reserved capacity (1-year or 3-year Savings Plans) for baseline compute, supplemented by on-demand or spot for peak demand. Trn1 instances should be evaluated seriously at this scale — the 50 percent cost reduction over P5 can save hundreds of thousands of euros per year if your models are compatible with the Neuron SDK.

Monthly cost estimate: 20,000 to 100,000 EUR with reserved pricing. On-premises equivalent (after staffing): 15,000 to 50,000 EUR amortized.

Large Scale: Foundation Model Training (64+ GPUs)

At this scale, the economics shift toward on-premises or hybrid. Continuous utilization of 64 or more GPUs drives cloud costs high enough that the capital investment in hardware pays back quickly.

Recommended approach: On-premises cluster for sustained baseline training, with cloud burst capacity for peak periods and experiments. The on-premises cluster handles long-running pretraining jobs, while cloud instances handle shorter fine-tuning runs, evaluation, and experiments that need different hardware profiles.

Monthly cost estimate: Cloud-only at this scale runs 200,000+ EUR per month. A hybrid approach with on-premises baseline can reduce this to 80,000 to 150,000 EUR total (including amortized hardware and staffing).

MLOps Advantages in Cloud

Beyond raw compute costs, cloud infrastructure offers MLOps capabilities that are difficult and expensive to replicate on-premises.

Experiment tracking integration. Services like SageMaker Experiments, Weights and Biases, and MLflow integrate natively with cloud training jobs. Metadata, metrics, and artifacts are captured automatically.

Data versioning and lineage. S3 versioning combined with tools like DVC or LakeFS provides reproducible data pipelines. On-premises storage typically lacks equivalent versioning capabilities.

Distributed training orchestration. EKS with Karpenter or SageMaker distributed training handles multi-node job scheduling, automatic scaling, and failure recovery. Building equivalent orchestration on-premises with Slurm or Kubernetes requires significant engineering investment.

Model registry and deployment pipeline. Training in the cloud produces artifacts that flow directly into cloud-native serving infrastructure. On-premises training adds a transfer step that creates latency and potential for drift between training and serving environments.

Data Pipeline Considerations

Where your training data lives is often the deciding factor in infrastructure placement.

If your data pipeline is cloud-native — S3 data lake, Glue or Spark ETL, streaming ingestion from cloud services — training in the cloud eliminates the data movement problem entirely. Training jobs read directly from S3 at high throughput.

If your data originates on-premises (lab instruments, proprietary databases, manufacturing sensors), the cost and latency of moving terabytes to the cloud for each training run may justify on-premises training. In this case, consider a hybrid approach: preprocess and store a training-ready dataset in S3 for cloud training, while keeping raw data on-premises.

AWS DataSync and Transfer Family can help bridge the gap, but sustained multi-terabyte transfers require dedicated network capacity (Direct Connect) to be practical.

Spot Instance Strategies for Training

Spot instances are the most underutilized cost optimization lever for ML training. The key is building fault tolerance into the training pipeline.

Checkpointing. Save model checkpoints to S3 every N steps. When a spot interruption occurs, the new instance resumes from the last checkpoint. Modern frameworks (PyTorch FSDP, DeepSpeed) handle distributed checkpointing efficiently.

Mixed instance fleets. Request capacity across multiple instance types and availability zones. A training job that can run on P4d, P5, or Trn1 has dramatically better spot availability than one locked to a single instance type.

Capacity-aware scheduling. Use spot placement scores to choose regions and AZs with the best availability. Shift non-urgent training jobs to off-peak hours when spot capacity is more abundant.

Savings Plans as a floor. Combine a Compute Savings Plan (covering your baseline utilization) with spot for peak demand. The Savings Plan provides guaranteed capacity at a discount; spot provides elastic overflow at the lowest possible price.

The Hybrid Approach

For most ML teams operating at moderate to large scale, the answer is not purely cloud or purely on-premises. The hybrid model provides the best combination of economics, flexibility, and operational simplicity.

On-premises handles sustained, predictable training workloads where utilization exceeds 70 percent over a multi-year horizon. This is your most cost-efficient compute.

Cloud handles bursty experimentation, short-lived training runs, workloads that benefit from Trainium economics, inference serving, and overflow capacity during peak demand.

The glue is a unified MLOps platform — typically Kubernetes-based — that abstracts the underlying infrastructure and lets researchers submit training jobs without caring whether they run on-premises or in the cloud. Kubeflow, Ray, and SageMaker HyperPod all support this pattern to varying degrees.

The critical requirement is a shared storage layer. S3 as the canonical data and artifact store, with on-premises caching (FSx for Lustre or a local parallel filesystem), gives you the flexibility to train anywhere without rewriting data pipelines.

Remangu designs and operates GPU infrastructure for AI/ML teams on AWS, from single-node fine-tuning setups to multi-node distributed training clusters. If you are evaluating your GPU infrastructure strategy, we can help you model the options.

GPU Infrastructure for ML Training: Cloud vs. On-Prem in 2026