Introduction

With the explosive rise of artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) applications, the world is witnessing an unprecedented strain on GPU availability as demand far exceeds current supply. This imbalance—fueled by chip shortages and supply chain issues—poses a serious challenge for teams needing high-performance compute resources. Innovation can stall when faced with procurement delays, scarce instance availability, and escalating costs.

But there’s good news: AWS offers a wide range of tools and strategies to help you deploy AI workloads efficiently, even when GPU availability is limited. In this guide, we’ll explore how to:

• Secure and manage GPU capacity effectively
• Leverage managed services like Amazon SageMaker
• Use purpose-built accelerators like AWS Trainium and Inferentia
• Explore non-GPU compute options
• Improve GPU utilization through sharing
• Implement monitoring and cost optimization strategies

Let’s dive into how to keep your AI projects on track and on budget—even when GPUs are hard to come by.

Strategic GPU Procurement on AWS

Plan Ahead with EC2 Reservations and Blocks

AI and ML training require serious compute power. AWS EC2 Accelerated Computing instances are designed for this, offering top-tier GPUs and custom silicon like Trainium and Inferentia. To ensure access when demand spikes, consider:

• On-Demand Capacity Reservations (ODCR) ensure that your critical workloads always have the compute capacity they need—when they need it.
• EC2 Capacity Blocks for ML: Reserve high-performance GPU clusters for 1 to 14 days—a great fit for short, intense training runs.

Save Big with Reserved Instances and Savings Plans

AWS rewards commitment. By opting for 1- or 3-year pricing agreements through Compute or EC2 Instance Savings Plans—or Standard/Convertible Reserved Instances—you can dramatically cut costs. These plans are ideal for long-running training jobs or persistent inference applications.

Harness the Spot Market

EC2 Spot Instances can reduce costs by up to 90%, making them ideal for budget-conscious workloads. The catch? They can be interrupted. But paired with resilient job orchestration tools or AWS’s managed services, Spot Instances are an excellent way to train and infer at scale for less. Even AWS Trainium and Inferentia instances are available on the Spot market.

Maximize Efficiency Through Consolidation

Pooling resources across teams or departments unlocks economies of scale. Use AWS Organizations to centralize billing, apply Savings Plans across accounts, and share Reserved Instances.

You can even consolidate GPU demand across hybrid environments—your on-premises data centers and cloud workloads—by:

1. Assessing usage patterns
2. Modeling total GPU costs
3. Negotiating unified procurement deals with AWS

This big-picture approach results in smarter spending and fewer idle resources.

Streamlining AI Development with Amazon SageMaker

Amazon SageMaker simplifies machine learning by managing infrastructure for training and inference.

HyperPod: Distributed Training Made Easy

SageMaker HyperPod helps you run large-scale training across clusters of smaller GPUs. With built-in failure recovery, checkpointing, and auto-scaling, it improves efficiency while cutting training times by up to 20%.

Managed Spot Training

Train models on Spot Instances without worrying about job interruptions. With SageMaker’s Managed Spot Training, tasks like checkpointing, retries, and scaling are handled seamlessly in the background.

Consider AWS AI Accelerators: Trainium and Inferentia

Trainium and Inferentia, custom-designed by AWS for AI, offer top-tier performance at a fraction of the cost of standard GPUs.

• Trainium is optimized for training deep learning models. It offers up to 50% lower training costs compared to GPUs for large-scale models like Llama or GPT-NeoX.
• Inferentia2 is specifically engineered for inference tasks, offering 4x the throughput and 10x lower latency than its predecessor. It’s ideal for deploying LLMs and GenAI workloads at scale.

Trainium and Inferentia work seamlessly with frameworks like PyTorch and TensorFlow via the AWS Neuron SDK, making it easy to train and deploy models efficiently on AWS infrastructure.

Mix and Match: Flexible AI Deployment

Not every workload needs to run entirely on the same hardware. Train your models using AWS Trainium and run inference on-premises—or do it the other way around. The choice is yours. This hybrid flexibility is perfect for:

• Meeting data residency or compliance needs
• Reducing cloud egress costs
• Balancing performance across environments

Neuron SDK allows for portability between environments so teams can build once and deploy anywhere, using the most cost-effective resources available.

Explore Alternative Compute Options

CPU for Inference

Many smaller models and batch inference workloads perform well on CPUs, which are often more cost-effective than GPUs. Use CPU instances for:

• Models with <1B parameters
• Batch or background processing
• Cost-sensitive use cases

AWS Graviton: High-Efficiency Inference

AWS Graviton processors are Arm-based and optimized for both performance and power efficiency. AWS Graviton3 chips are tailored for next-gen inference, enabling Hugging Face and PyTorch models to achieve up to double the performance of legacy CPU instances.

These instances work well for NLP, fraud detection, and recommendation systems—especially when paired with SageMaker for a fully managed workflow.

Increase Utilization: Share GPU Resources

Don’t let idle GPU capacity go to waste. By tapping into services like AWS Batch, Amazon ECS, and Amazon EKS, you can streamline workload execution with minimal operational overhead.

• Run multiple GPU-accelerated containers per instance
• Automatically scale GPU resources to match real-time workload requirements

For even more efficiency, use NVIDIA’s Multi-Instance GPU (MIG) or GPU Time-Slicing on Bottlerocket OS to divide GPU time across multiple jobs. This is especially useful for serving lightweight models concurrently on the same GPU.

Control Cloud Spend with Proactive Oversight and Smart Monitoring

Cost visibility is key to long-term optimization. Use:

• Amazon CloudWatch for performance monitoring
• AWS Budgets to set spending limits
• Cost Explorer for historical analysis
• Cost Anomaly Detection for proactive alerts

These tools give you control over usage, help forecast costs, and allow you to act before overruns occur.

Final Thoughts

GPU shortages may be a current challenge, but smart architecture and cost-aware deployment strategies can future-proof your AI workloads. By combining flexible compute options, leveraging AWS’s purpose-built accelerators, and making full use of managed services like SageMaker, organizations can:

• Maintain productivity despite supply constraints
• Control infrastructure costs
• Lay the foundation for scalable, sustainable AI operations

Whether you’re training cutting-edge LLMs or deploying real-time GenAI applications, these strategies will help you deliver value faster—without overspending.

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• Gain real-time insights into GPU usage and idle capacity
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Take control of your AI infrastructure spending—visit www.trucost.cloud to learn more and get started today.