GPU Resource Management for Kubernetes Workloads: From Monolithic Allocation to Intelligent Sharing

AI/ML workloads are evolving rapidly in complexity, diversity, and scale. As organizations increasingly deploy inference and training workloads in Kubernetes environments, it has become clear that conventional GPU allocation models can seriously undermine the efficiency of GPUs—a critical concern when comparing the relatively high cost of GPUs to their equivalent CPUs. 

The Limits of Traditional GPU Allocation 

Many Kubernetes workloads request far more resources than they need. Overprovisioning is a well-known issue in conventional CPU environments and is only exacerbated in GPU environments, where pods typically request GPUs in whole units—whether it’s for a quick Jupyter Notebook experiment or a large training workload. While this practice provides each workload with strong isolation, it also means significant GPU resources sit idle. In fact, it has been estimated that 60% of GPU resources regularly go to waste—inflating operational costs, limiting throughput, and increasing the total cost of ownership for GPU infrastructure. 

In an ideal world, developers would provision exactly as much GPU as needed by their pods, but the complexity of modern software environments means developers often don’t know how much is needed, especially when those needs vary dynamically. 

To help address GPU overprovisioning, interest is growing in new GPU partitioning technologies that enable multiple workload processes to share a single GPU. As described below, several approaches to GPU sharing exist today. Each comes with tradeoffs in terms of tenant isolation, performance and complexity—complicating the choice of approach. Moreover, any GPU sharing approach usually requires significant effort from the platform engineering team to implement and manage. 

Fortunately, there are efforts underway toward more intelligent, automatic, workload-aware GPU resource management. In this new model, GPU resources are accurately rightsized and partitioned according to the compute and memory requirements of individual workloads, all while ensuring that sharing is done safely in a busy and contentious GPU environment. 

Intelligent GPU Allocation: A New Paradigm 

Intelligent GPU allocation means that, rather than assigning GPUs as monolithic units, the system can automatically match workloads to slices or classes of GPU resources according to the resource needs of those workloads.  

With NVIDIA GPUs, the infrastructure team can choose among sharing modes like MIG (multi-instance GPUs), MPS (multi-process services), time-slicing, or even exclusive allocation if needed for a particular workload:  

• MIG provides strong isolation as a hardware partition but lacks flexibility for elastic and on-demand resources often required by AI workloads. However, MIG is hardware-limited to NVIDIA A100 and newer GPUs and can be challenging to configure and change dynamically. 

• MPS improves GPU throughput and efficiency by allowing multiple processes to share a GPU, but it offers weak isolation and no memory guarantees.  

• Time slicing is flexible but can also result in inefficient resource allocation across different workload types, as well as unpredictable execution windows that lead to inconsistent performance. 

Deciding which sharing model to apply is a strategic design choice. For example, inference or test workloads with light but bursty GPU usage may benefit from time-slicing or MPS. Large-scale training jobs requiring consistency and isolation may benefit from either MIG or whole-GPU exclusive allocations. 

Over time, more sophisticated solutions will likely be developed that take on the effort of managing GPU assignments automatically. 

Broader Trends and Implications 

The cloud transformed computing by abstracting it from individual resource units to an operational plane that obscures the underlying hardware. Similarly, the Kubernetes ecosystem is undergoing deeper integration with hardware abstractions, and the Kubernetes scheduling plane is becoming more informed and expressive, with workloads able to describe their needs in greater detail. Kubernetes support for GPUs is also evolving rapidly.  

Along these same lines, we are now starting to see GPU allocation evolve to become more abstracted and intelligent, where GPUs can be shared efficiently among multiple AI workloads, maximizing throughput and efficiency and minimizing latency.  

All of these trends are essential for running mixed high-performance AI workloads at scale. 

The Imperative to Invest in Intelligent GPU Allocation 

Kubernetes spend is expected to exceed $2.57 billion this year and continue to expand at a CAGR of 22.4%, driven in no small part by the rise of AI workloads. While AI comes with significant costs, the greater risk lies in ignoring it and falling behind. Organizations that continue to invest in AI today are establishing the data, talent, and infrastructure for future success.  

Equally important is the commitment to tools, techniques, and processes that effectively manage the increasing GPU spend driven by AI. Maximizing GPU resource utilization for Kubernetes workloads is no longer a nice-to-have; it’s a requirement. Even a small efficiency improvement in a GPU environment can have an outsized financial impact, enabling further investment for growth. The industry shift toward intelligent GPU allocation reflects this demand for more cost-effective GPU allocation solutions.  

By wisely conserving cost and other resources required to develop and deploy new AI workloads, organizations will be able to take full advantage of the stunning technological and business advances that AI continues to offer. 

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