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Rethinking GPU Allocation in Kubernetes

Kubernetes has cemented its position as the de-facto standard for orchestrating containerized workloads in the enterprise. In recent years, its role has expanded beyond web services and batch processing into one of the most demanding domains of all: AI/ML workloads.

Organizations now run everything from lightweight inference services to massive, distributed training pipelines on Kubernetes clusters, relying heavily on GPU-accelerated infrastructure to fuel innovation.

But there’s a problem. In this blog, we will explore why the current model falls short, what a more advanced GPU allocation approach looks like, and how it can unlock efficiency, performance, and cost savings at scale.

The Current State: Traditional GPU Allocation

Today, GPUs in Kubernetes are scheduled using a straightforward, integer-based model:

  • Pods request GPUs via resource requests such as nvidia.com/gpu: 1.
  • The scheduler treats GPUs as opaque, black-box devices.
  • Each workload receives exclusive access to the entire GPU, regardless of its actual needs.
  • There is no awareness of memory consumption, compute usage, or GPU topology.

Important

On the surface, this model looks clean and manageable. Each workload gets one or more “whole GPUs,” and the complexity of sharing is avoided. But simplicity comes at a steep cost.

Why This Model is Misaligned with AI Workloads

While Kubernetes has evolved rapidly in many areas, its GPU scheduling model remains surprisingly primitive. GPUs are treated as indivisible units, allocated in whole numbers to individual workloads.

This traditional approach, while simple to implement, is fundamentally mismatched with the diverse and dynamic requirements of modern AI workloads.

The result: underutilization, inefficiency, and operational headaches

Modern AI workloads vary dramatically in their resource requirements.

1. Inference Jobs

Many inference workloads require just a fraction of a GPU’s resources. Sometimes, 2–4 GB of GPU memory is sufficient. Yet, under the traditional model, these jobs are assigned entire high-capacity GPUs like an 80 GB A100, leaving most of the GPU resources idle.

2. Distributed Training Jobs

Large-scale training requires multiple GPUs with high-bandwidth interconnects (such as NVLink). Without topology awareness, the scheduler may scatter GPUs across nodes or ignore connectivity, leading to poor performance and longer training cycles.

3. Mixed Workloads

In real-world environments, organizations often run a mix of inference, training, and batch jobs. These could, in theory, share GPUs efficiently. Instead, they are forced onto separate devices, reducing utilization and increasing cost.

The outcome is predictable: wasted GPU capacity, inflated infrastructure bills, and frustrated engineering teams

Reality Check- An Example

Consider a scenario where a Kubernetes cluster with multiple nodes hosting 10 GPUs, each with 80 GB of memory.

If an organization deploys 20 inference jobs, each requiring only 4 GB, the cluster should have no problem accommodating them. In theory, all 20 could run on a single GPU with room to spare.

Scenario 1

Instead, under the traditional model, each job consumes an entire GPU, meaning only 10 jobs can run — leaving the cluster at 50% capacity while the GPUs themselves are only ~5% utilized.

Scenario 2

Now flip to the other extreme: a distributed training job that requires eight tightly connected GPUs. The scheduler, unaware of GPU interconnects, might assign GPUs scattered across multiple servers. This not only hurts performance but can make the workload infeasible altogether.

In both cases, the gap between workload needs and scheduling capability becomes painfully obvious.

The Opportunity: Rethinking GPU Allocation

It’s clear that the traditional “one workload, one GPU” approach is no longer sustainable.

The question is: what does the future look like?

The answer lies in advanced, workload-aware GPU allocation — a model that recognizes the diversity of AI workloads and aligns scheduling decisions with actual resource requirements.

Conclusion

Kubernetes has proven itself as the foundation for modern AI infrastructure, but its GPU scheduling model is due for a transformation. The traditional whole GPU approach, while simple, wastes resources, limits performance, and mismatches the needs of real workloads.

By adopting advanced GPU allocation — fractional assignments, topology awareness, shared utilization, and dynamic scaling — organizations can unlock the full potential of their infrastructure. The result is not just technical efficiency, but real business impact: lower costs, faster innovation, and the ability to scale AI with confidence.

In the next blog, we will look at how Dynamic Resource Allocation (DRA) coming in Kubernetes 1.34 addresses many of these challenges.