How to Stop Wasting GPU Resources: A Complete Guide to Optimizing Kubernetes-Based ML Infrastructure

How to Stop Wasting GPU Resources: A Complete Guide to Optimizing Kubernetes-Based ML Infrastructure

"Why is GPU utilization in our cluster so low?"

If you are a machine learning engineer, MLOps engineer, or cloud architect, you have probably spent time hunting for an answer to that question. As demand for training and inference of the latest LLMs (large language models) has exploded, GPUs have become the heart of AI infrastructure. The frustration of watching that heart underperform—expensive resources sitting idle—is hard to overstate.

GPUs are truly high-value resources. Going beyond simply “deploying” them and building an architecture that uses them at maximum efficiency and scales them reliably is the most important challenge in modern AI infrastructure.

This guide goes beyond a simple deployment walkthrough. It offers a practical technical roadmap for eliminating GPU waste at the source and deploying complex ML workloads onto a stable, scalable cluster.

🚀 1. Why ML Infrastructure Optimization Is Essential (The GPU Waste Problem)

The problem we usually face is not a shortage of resources, but inefficient resource allocation.

GPUs are optimized for parallel processing, so once a large training job starts, it tends to monopolize those resources. The problem is that GPU resources often are not used at 100% until that job finishes.

[Example of the problem]

1. GPU allocation: You assigned one A100 GPU with 80GB of VRAM to a Pod. (Cost is incurred.)
2. Actual usage: During model inference or data preprocessing, only 30% of the resource is used; the remaining 70% sits idle.
3. Result: The GPU is powered on, but it delivers only about 30% of its value. That wasted 70% is the “cost” we need to solve.

Beyond simply spinning up Pods, you need sophisticated orchestration that allocates resources only as much as needed, only when needed.

⚙️ 2. Characteristics of ML Workloads and the Limits of Vanilla K8s

ML workloads are fundamentally different from typical web-service workloads.

• Massive parallelism: They use many cores and a large amount of memory at the same time.
• Memory dependence: Model weights themselves consume enormous amounts of memory.
• Long runtimes: Training can last from hours to days.

Because of these characteristics, vanilla Kubernetes (K8s) resource allocation hits a wall. By default, K8s treats resources as whole units.

If there is one GPU, K8s sees it as “1,” and when a Pod requests that 1, it assigns the entire GPU. It is like leasing an entire large apartment when the tenant only uses a single room.

This is where the core concepts come in: GPU virtualization and partitioning.

💡 Key concept: GPU virtualization splits physical GPU resources into small, logical, independent units (slices) so that multiple smaller workloads each appear to have their own dedicated GPU.

🛠️ 3. The Kubernetes GPU Resource Optimization Stack

To implement this “slicing” in a K8s environment, you need to understand three core technologies.

3.1. GPU Device Plugin: The First Step So K8s Can “See” GPUs

The K8s scheduler natively understands only standard resources such as CPU cores and memory. To use specialized hardware like GPUs, you have to tell K8s how many GPUs are in the cluster and what types they are.

That is the job of the Device Plugin.

How it works (flow):

1. Install the plugin: Deploy the NVIDIA Device Plugin to the cluster.
2. Node reporting: The plugin contacts each worker node and reports to the K8s API server, e.g. “This node has 4 A100 GPUs.”
3. Scheduling: When a user requests nvidia.com/gpu: 1 in a Pod spec, the K8s scheduler receives that request and places the Pod on a node with enough capacity.

✅ Hands-on example: Requesting a GPU in a Pod spec

apiVersion: v1
kind: Pod
metadata:
  name: gpu-worker
spec:
  containers:
  - name: ml-container
    image: your-ml-image:latest
    resources:
      limits:
        # Device Plugin을 통해 인식된 자원을 요청합니다.
        nvidia.com/gpu: 1 

3.2. NVIDIA MIG (Multi-Instance GPU): The Most Important Partitioning Technology

If the Device Plugin only tells K8s that “GPUs exist,” MIG decides how to slice them.

NVIDIA MIG splits a single GPU into multiple fully isolated smaller GPU instances. Each instance has its own memory, compute units, and cache, with no resource interference between instances.

✨ An analogy:

• The whole GPU: A large 100-unit office building.
• Default allocation: Lease the entire building to tenant A. (Even if A uses only 10 units, they pay for all 100.)
• With MIG: Split the building into 10 independent offices, each with its own power, HVAC, and security. (You rent only the 10 units you need; the other 90 can be leased to someone else.)

Benefits:

1. Isolation: If one workload becomes unstable, others are unaffected. (Maximum stability)
2. Maximum utilization: You can split one GPU into 4, 8, or more small units and run multiple jobs at the same time.

3.3. Workload Distribution Strategy: Optimizing Resource Allocation

Modern workloads are rarely a single job. Multiple small model inference jobs can arrive at the same time. That is when you need GPU partitioning: splitting one physical GPU into multiple logical resources and allocating them independently.

🚀 Summary of a Practical Rollout

💡 Conclusion: A Paradigm Shift in Resource Utilization

In the past, the dominant mindset was “this job must monopolize the entire GPU.” For modern AI workloads, slicing resources into smaller fragments is the key.

Instead of consuming a GPU whole, splitting it into small units such as a quarter GPU or an eighth GPU and assigning those slices to multiple users is directly tied to maximizing cloud compute efficiency. Understanding this concept and applying it in Kubernetes is a core skill for building modern AI infrastructure.

References: Official documentation

The primary source for the behavior, configuration, and errors discussed in this article is the following official documentation. Check it for version-specific options and exact behavior.

• Kubernetes official documentation