Modern artificial intelligence and HPC (high-performance computing) workloads rely on the immense parallel processing capabilities of GPUs (Graphics Processing Units). Did you know that many GPUs operate at only 15% of their capacity, resulting in thousands of dollars wasted in potential ROI? Possessing GPUs is only the first step. You must use GPU optimization strategies to unlock their full potential.
This article explains the problem of GPU underutilization and introduces three core strategies to address it:
The Problem of GPU Underutilization
As GPU technology advances, the gap between its potential and actual usage widens. But optimizing GPU usage isn’t just a technical goal; it has become a crucial business driver. This is because even the most advanced GPUs often operate at a fraction of their capacity, sometimes as low as 15%. This leads to significant inefficiencies and wastes capital.
A single AI model or computing task rarely requires the full capacity of a top-tier GPU, yet the standard “one job, one GPU” rule often prevails. This practice results in expensive hardware sitting idle for significant periods, consuming power and generating heat without delivering productive output.
Several factors contribute to underutilization. Legacy schedulers may lack the sophistication to manage resources efficiently, while certain workloads, like AI inference, only need a fraction of a GPU’s power.
Without an effective method to partition and share GPU resources, organizations will inevitably face significant inefficiencies and fail to realize the full value of their substantial investments in GPU infrastructure.
This lack of optimization not only undermines performance but also results in wasted capital, power consumption, and missed opportunities for scaling critical workloads, making it nearly impossible to justify the costs associated with large-scale GPU deployments.
Proper optimization turns idle capacity into an opportunity for growth and performance gains.
Core Strategy 1: How Hardware Isolation Boosts GPU Efficiency
The most direct method for improving GPU utilization is hardware isolation. This technique involves partitioning a physical GPU at the silicon or firmware level, creating multiple smaller, dedicated hardware units from a single device.
Each partition, or slice, operates as an independent GPU with its own dedicated compute cores, memory, cache, and bandwidth.
This approach guarantees strong performance isolation and Quality of Service (QoS). Workloads running on separate “slices” cannot interfere with one another.
By establishing these strict boundaries, organizations can run multiple workloads concurrently on a single GPU, thereby boosting overall utilization. Hardware isolation is particularly effective for multi-tenant environments and use cases where predictable performance is non-negotiable.
Core Strategy 2: The Benefits of Virtualization for GPU Optimization
GPU virtualization offers a more flexible approach to resource sharing. By leveraging hypervisors and standards like Single Root I/O Virtualization (SR-IOV), a physical GPU can be shared across multiple virtual machines (VMs) or containers.
Unlike the fixed partitions of hardware isolation, virtualization allows for the dynamic allocation and reallocation of GPU resources based on workload demand.
This method provides several key benefits:
| Greater Flexibility | Administrators can adjust resource assignments on the fly without needing to shut down hardware. |
| Fine-Grained Control | Sizing can be tailored to the specific needs of a job, often defined by the user at the time of submission. |
| Simplified Management | Workloads can be migrated between hosts, and resource allocation changes are streamlined through software. |
Virtualization transforms a rigid, physical resource into a fluid, composable asset. It is ideal for environments with mixed workloads and fluctuating demands, enabling cloud-like elasticity for on-premises GPU clusters.
Core Strategy 3: Software-Driven GPU Orchestration
Hardware isolation and virtualization provide the foundation for partitioning GPUs. However, a software layer is necessary to manage these resources at scale. Software-driven orchestration platforms sit atop the hardware and virtualization layers, providing advanced scheduling, monitoring, and management capabilities that unlock further efficiencies.
These are common GPU orchestration tools:
| Fair-Share Usage and Quotas | Ensures equitable resource distribution among different users, teams, or projects. |
| Oversubscription | Allows users to access idle GPU resources beyond their assigned quotas, maximizing cluster-wide utilization. |
| Job Prioritization and Preemption | Guarantees that critical workloads are executed first, with the ability to checkpoint and resume lower-priority tasks later. |
| Heterogeneous Pooling | Manage clusters containing various GPU models and generations, assigning workloads to the most appropriate hardware. |
By adding this layer of intelligence, organizations can automate and optimize resource allocation, ensuring that every GPU cycle contributes to business value.
Conclusion
Maximizing GPU utilization is no longer just a technical consideration. It is fundamental to achieving operational efficiency, reducing costs, and driving business innovation in AI and data-intensive environments. By understanding and implementing a combination of hardware isolation, virtualization, and software orchestration, organizations can build a highly efficient, scalable, and cost-effective GPU infrastructure.
If you want to unlock the full potential of your GPU resources or need guidance on modernizing your infrastructure, HighFens can help.
Contact us today for a tailored consultation on GPU optimization.