GPU Virtualization: Maximizing Efficiency for AI, ML, and HPC Workloads

As organizations adopt AI, machine learning, and data-intensive workloads, GPUs have become critical infrastructure. But with their high cost and power demands, suboptimal utilization creates wasteful idle time. Virtualization has emerged as a powerful solution for maximizing GPU efficiency, enabling multiple workloads to share GPU resources driving higher utilization and efficiency.

In this post, we’ll explore how GPU virtualization works, the main approaches available today, and the benefits and constraints this approach delivers for enterprises looking to optimize GPU utilization.

GPUs are designed for massive parallelism, but workloads often fail to utilize them fully. Reasons for this include using legacy schedulers, inefficient code and lack of checkpointing.

For example:

• Deep learning training might need to use the entire GPU for a limited period, which leaves it idle during a long-running job. With proper check pointing and the ability to time slice the GPU, smaller workloads can take advantage of the available GPU cycles.
• Inference tasks that only require a fraction of a GPU create large amounts of waste if assigned the entire GPU.
• Mixed workloads such as research, visualization, and HPC compete for resources but can’t easily share them

Without an optimization layer, GPU infrastructure ends up underutilized. This drives up cost per workload.

GPU virtualization enables fine-grained resource allocation. Multiple applications or users can run simultaneously on a single GPU

There are three primary approaches:

The GPU is shared across workloads by assigning them different time slots. Each process gets repeating “bursts” of GPU execution.

The figure below illustrates Fair time-slicing mode.

Since the overall runtime of a workload may be extended this approach is best for interactive or latency-tolerant workloads, including model training and inference batch processing.

Hardware isolation was discussed in detail in the last blog however it is relevant here as well since GPU slices created using vendor software like NVIDIA multi-instance GPU (MIG) or AMD MxGPU can be utilized by virtual servers.  It should be noted that the hypervisor must provide support for this functionality.

• In this method a full GPU is assigned to a single virtual host, and only workloads running on that specific virtual host have access to the GPU.  This provides isolation of the GPU from other hosts.
• There is a small amount of overhead using a hypervisor, but this approach allows near-native performance with the flexibility of a virtualized infrastructure.

There is a range of advantages to be had from virtualizing GPUs. Here are a few examples:

1. Higher Utilization Rates: Multiple workloads can share a GPU, reducing idle time and driving higher utilization.
2. Cost Optimization: Enterprises can consolidate workloads and defer the expense of additional hardware purchases.
3. Workload Flexibility: Match GPU resources to the exact needs of training, inference, or visualization tasks.
4. Scalability: Allows Cloud-like elasticity for on-premises GPU clusters.

Here is a list of real-world virtual GPU use cases:

GPU virtualization transforms GPUs from a rigid, siloed resource into a flexible, shared, and highly optimized computing layer. For organizations investing in AI and HPC, leveraging virtualization means higher ROI, lower costs, and greater agility.

GPU hardware will continue its rapid evolution, so virtualization tools must also become more sophisticated. NVIDIA’s MIG, AMD’s SR-IOV-based GPU partitioning, and orchestration tools like Kubernetes with GPU operators are paving the way for dynamic, automated GPU allocation. The future will likely see GPUs treated as fluid, composable resources, just like CPU and memory today.

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.