GPUs are the foundation of modern machine learning (ML), they do the parallel computations needed to train complex models efficiently. They can process thousands of tasks at the same time so they are essential for big data and deep learning workloads. But this power comes with a big cost, especially in cloud where GPU instances are priced by usage. Inefficient GPU usage can be very costly, some studies say almost a third of GPU users are running at less than 15% utilization. Optimizing GPU usage is key for organizations that want to manage costs while scaling their MLOps pipelines.
This blog post explores the importance of GPU optimization, identifies common causes of GPU waste, and provides actionable best practices to enhance efficiency and reduce costs. We’ll also discuss relevant tools and frameworks and cost management strategies. Whether you’re a data scientist, ML engineer or engineering manager these will help you get the most out of your GPUs.
Why GPU Optimization Matters
Inefficient GPU usage has both financial and operational consequences. Financially, underutilized GPUs mean paying for compute power that isn’t fully leveraged. For example a team spending $50,000 a month on cloud GPU’s might be using only a fraction of their capacity and wasting thousands of dollars. Operationally poor GPU management slows down development cycles, delays model deployment and limits resource availability for critical tasks. As AI workloads grow efficient GPU usage is key to being competitive and sustainable.
Imagine a data science team provisions multiple high end GPU’s for a project but uses them at 20% capacity because of inefficient code or scheduling. By optimizing their workflows they could get the same results with fewer resources and save costs and free up GPU’s for other tasks. Optimizing GPU usage can save a lot in many cases so it’s a high impact area for MLOps teams.
Common Causes of GPU Waste
Understanding the root causes of GPU waste is the first step toward optimization. Here are the most common culprits:
- Overprovisioning: Unnecessarily using the GPU bigger than one needs, say an A100 for a job that can run on a smaller GPU.
- Poor Scheduling: These will keep GPUs idle for a while, waiting for jobs to be submitted, thus wasting compute time.
- Inefficient Code: If a code uses a small fraction of all GPU cores, or experiences memory bottlenecks, the effect is detrimental to performance.
- Lack of Monitoring: Without tools that track GPU utilization, you might lose sight of patching inefficiencies.
For instance, a team provisions a cluster of GPUs for a training job and leaves them idle between jobs due to poor scheduling. Or unoptimized code may fail to parallelize operations and leave GPU resources lying around.
Best Practices to Optimize GPU Usage
To maximize GPU efficiency and reduce costs, consider these best practices:
Profile and Benchmark Workloads
Profiling tools like NVIDIA Nsight and PyTorch Profiler inform you of your GPUs performance and let you know if your workloads are compute-bound versus memory-bound. Using information on memory usage, compute utilization, etc., you will be able to improve your code to make better use of the GPU.
Use Autoscaling and Spot Instances
Cloud providers like AWS, Azure, and Google have autoscaling to scale GPU resources up or down based on demand and save on costs during low utilization periods. Spot instances which provide surplus GPU capacity at a significant discount to on-demand pricing are great for interruptible jobs like hyperparameter tuning. But spot instances require robust checkpointing to handle interruptions.
Right-Size GPU Resources
Choosing the right GPU and quantity for the job prevents overprovisioning. For example, T4 is sufficient for inference tasks, while large-scale training leans towards the A100. Start small and scale accordingly, or else you will have wasted money.
Optimize Data Pipelines
The actual bottleneck for GPUs lies in data loading and preprocessing. Prefetching presents one of the best solutions; in addition to that, faster storage (e.g., SSDs) and parallelization of data processing help keep the GPU happily busy. Any savings in I/O directly translate into savings in training time and thus costs.
Leverage Mixed Precision Training
Mixed precision training with a low precision format such as FP16 as opposed to FP32 speeds up the computation and reduces memory usage. This enables larger batch sizes or models to fit on a single GPU and perform more efficiently. All deep learning frameworks including TensorFlow and PyTorch support automatic mixed precision.
Implement Job Scheduling and Queuing
Efficient job scheduling minimizes GPU idle time. Such tools as Slurm or Kubernetes manage the job queue so that tasks are executed promptly. For example, Kubernetes GPU scheduling allows dynamic allocation of GPUs to active jobs to reduce waste of money.
Monitor and Alert on GPU Utilization
By utilizing monitoring tools for GPU utilization, memory utilization, and power consumption such as Prometheus, AWS CloudWatch, or third-party tools like Kubecost with alerts set for low utilization (below 50%), a team can quickly spot and solve inefficiencies.
Use Multi-Instance GPUs (MIG)
NVIDIA’s Multi-Instance GPU (MIG) feature on GPUs like A100 and H100 allows a single GPU to be partitioned into multiple isolated instances. This is perfect for running multiple small jobs concurrently and reduce costs.

Tools and Frameworks
Several tools and frameworks can streamline GPU optimization in MLOps:
- Kubernetes GPU Scheduling: It allows us to efficiently allocate and share GPU resources with Kubernetes clusters in order to minimize idle time.
- Kubeflow: An open-source platform for machine learning in Kubernetes to ease model development, training, and acquisition of insights into resource usage.
- MLflow: Handles all parts of the ML lifecycle, from experiment tracking to model deployment, so that we have no duplicate jobs wasting GPU resources.
- NVIDIA Libraries: Libraries such as cuDNN for deep learning and TensorRT for Inference optimization augment GPU performance.
Several tools and frameworks can streamline GPU optimization in MLOps:
As for cloud-specific tools, an example is AWS SageMaker which provides managed GPU instances with built-in optimizations, while experiment trackers such as Weights & Biases or Neptune.ai inform resource usa
Cost Management Strategies
Controlling GPU costs requires proactive strategies:
- Budgets: Set spending limits in cloud platforms to monitor and cap GPU expenses. For example, AWS Budgets alert teams when costs exceed thresholds.
- Tagging: Cost allocation tags should be used so that GPU resources can be tagged according to projects, teams, or workloads, to facilitate detailed cost tracking. This enables identification of major expenses to work on for optimization.
- Reserved Capacity: Committing long-term usages (such as 1 to 3 years) can give discount to the cloud GPU instance; it is ideal for workloads with a certain predictability.
- Cost Optimization Tools: Tools such as AWS Cost Explorer, Azure Cost Management, or even third-party tools such as CloudHealth, are used to find ways of analyzing where costs go and where potential savings are.
The application of these strategies gives teams the visibility of GPU costs, allowing them to make cost-reduction decisions.
Conclusion
Reducing GPU usage is key to saving costs and efficiency in MLOps. You save money when you address the usual causes of waste, which include providing too much, scheduling poorly along with writing bad code. Certain methods, such as examining workloads, using extra computer power, and improving data routes, help you reach this goal. Tools like Kubernetes, Kubeflow as well as NVIDIA libraries also help. You manage expenses better with cost plans like budgets, tags in addition to reserved capacity.
Start by checking how much you use your graphics processing units. You can do this with profiling tools. Try out ways to save money, like using extra computer power. For more material about improving your MLOps workflows, or for personal help, go to our website or call our team.