Stop Overspending: 7 Proven Ways to Optimize GPU Costs for AI & ML

Artificial intelligence is revolutionizing industries, but the high cost of GPU computing can be a barrier. Are you looking for practical strategies to reduce your GPU expenses without sacrificing performance? Explore these actionable tips for GPU cost optimization.

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What is GPU Cost Optimization and Why Does It Matter?

GPU cost optimization is efficiently using GPU resources to minimize expenses in AI and machine learning projects. This minimizes your total cost of ownership. Given the high cost and power consumption of powerful GPUs this is essential for operational efficiency.

Want advice on optimizing your GPU performance? Check out our article on boosting performance, leveraging hardware features, and programming languages!

7 Actionable GPU Cost Optimization Strategies for AI/ML Workloads

Ready to cut GPU costs? First, assess your current expenses. Then implement a strategy addressing the biggest issues. Combining multiple strategies helps you maintain great performance and improve cloud ROI.

1. Smart Allocation: When to Choose CPU vs. GPU

CPUs excel at sequential, complex tasks. GPUs dominate parallel, repetitive calculations essential for AI/ML such as neural network training. Since GPU instances are pricier choose wisely so you don't waste money. Structure your AI/ML pipeline to reserve GPUs for deep learning training and inference, using CPUs for other operations.

CPU Use Cases:

• Data preprocessing and cleaning, needing complex, sequential logic.
• Feature engineering with intricate calculations.
• Hyperparameter tuning for smaller models.

GPU Use Cases:

• Training large neural networks.
• Batch processing of image or video data.
• Complex simulations with parallel computation.

2. Leverage Spot Instances and Preemptible VMs

Spot instances offer discounted surplus GPU capacity, ideal for cost-sensitive AI/ML. Preemptible VMs are similar and available for up to 24 hours. Implement checkpointing to save progress, as these can be reclaimed with little notice. The payoff is reduced costs for AI/ML initiatives.

3. Maximize Savings with Committed Use Discounts

Cloud providers usually show GPU pricing in hourly rates. This can be misleading compared to long-term usage. For sustained GPU needs, explore annual pricing options or committed use discounts that can provide significant savings, often 20-30% compared to on-demand rates.

DigitalOcean's GPU droplets, featuring NVIDIA H100 GPUs, start at just $2.50/hour with a 12-month commitment. Contact our sales team for more details.

4. Right-Size Your GPU Instances

Avoid defaulting to the most powerful GPU instances. It often leads to wasted resources. Analyze your workload's memory needs, processing power, and utilization requirements. Choose instances that match your workloads and prevent overspending.

For example, NVIDIA T4 GPUs are sufficient for inference while NVIDIA A100 GPUs are better suited for demanding training tasks.

5. Unlock Efficiency with Multi-Instance GPUs (MIG)

NVIDIA Multi-Instance GPU (MIG) partitions a single GPU into smaller, isolated instances. This maximizes utility by running multiple workloads on one GPU allowing you to lower your GPU cloud costs. MIG is ideal for inference tasks or lightweight training where smaller GPU slices suffice.

6. Monitor and Analyze GPU Utilization

Comprehensive monitoring is crucial for GPU cost optimization. Use cloud provider tools to gain insights into GPU performance, and identify underutilized instances. Regular analysis informs decisions about instance sizing and scaling. Set up custom dashboards and alerts to respond to trends. Monitoring efforts ensures optimal performance.

Key Metrics to Monitor:

• GPU utilization percentage
• GPU memory usage
• Power consumption
• CUDA memory allocation
• Tensor core utilization
• Number of concurrent GPU processes
• GPU error rates and types
• Job queue length and wait times

7. Negotiate Directly with Cloud Providers

Cloud providers often have flexible GPU pricing. Talk to their sales team to uncover savings. Prepare with usage patterns and project duration. They might offer custom pricing, volume discounts, or longer-term commitments. Discuss hourly rates, upfront payments, data transfer costs, and access to newer GPU models.

Accelerate Your AI Projects with DigitalOcean GPU Droplets

Leverage NVIDIA H100 GPUs for AI and machine learning. DigitalOcean GPU Droplets provide high-performance computing resources for model training and data processing. They also help you scale AI projects without complexity.

Key Features:

• NVIDIA H100 GPUs for great performance.
• Flexible configurations (single to 8-GPU setups).
• Pre-installed Python and Deep Learning software.
• High-performance local boot and scratch disks.

Sign up today and unlock the possibilities of GPU Droplets. For custom solutions, larger GPU allocations, or reserved instances, contact our sales team to learn how DigitalOcean can power your most demanding AI/ML workloads.

Try DigitalOcean for free

Click below to sign up and get $200 of credit to try our products over 60 days! Sign up