Your Kubernetes cluster runs a mix of AI workloads. Some tasks, like large language model training, require dedicated access to whole GPUs. Other tasks, like lightweight model inference or development notebooks, only need a fraction of a GPU's compute power. To maximize hardware utilization and dynamically allocate resources for both types of jobs, which configuration should you implement?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal: GPUs are expensive, and letting them sit idle is a great way to get a stern talk from your boss. If you've got a developer running a simple Jupyter notebook, they don't need a whole NVIDIA H100 to themselves—that's like driving a semi-truck to buy a gallon of milk! But your heavy training jobs do need that full horsepower. To balance this, we deploy the NVIDIA GPU Operator on Kubernetes and turn on GPU sharing. With technologies like Multi-Instance GPU (MIG) or Time-Slicing, we can slice a single physical GPU into multiple smaller virtual GPUs. The GPU Operator makes this super clean to manage, letting you assign dedicated hardware to the heavy lifters while sharing the rest among the smaller tasks. Trust me, your utility bill and your developers will thank you.
Full explanation below image
Full Explanation
In an enterprise AI infrastructure, managing GPU utilization efficiently is critical due to the high cost and power consumption of accelerator hardware. Workloads are often heterogeneous: training deep learning models requires massive parallel processing power and full GPU memory bandwidth, whereas inference tasks, testing, and debugging require far fewer resources.
To address this, administrators utilize the NVIDIA GPU Operator in conjunction with Kubernetes. The NVIDIA GPU Operator automates the deployment of all NVIDIA software components needed to provision GPUs. To optimize utilization, the Operator can be configured to enable GPU sharing. This is achieved through two primary technologies: 1. Multi-Instance GPU (MIG): Available on NVIDIA Ampere and Hopper architectures, MIG partitions a single GPU physical instance into up to seven independent GPU instances. Each instance has its own dedicated memory and SMs (Streaming Multiprocessors), guaranteeing hardware-level isolation and predictable performance. 2. Time-Slicing: This technique allows multiple CUDA applications to share a single GPU by interleaving their execution over time. While it does not offer hardware-level isolation like MIG, it is highly effective for oversubscribing GPUs for development environments and light inference.
Let's look at why the other options are incorrect: Option A is incorrect because static node affinity allocating one GPU per pod results in massive underutilization. Lightweight pods will reserve an entire GPU, leaving its compute cores idle and preventing other workloads from running. Option B is incorrect because FIFO (First In, First Out) scheduling running one job at a time destroys cluster concurrency. It creates massive queue delays and leaves the rest of the cluster's GPUs idle while a single-GPU job is running. Option D is incorrect because adjusting CPU limits does not solve GPU allocation issues. Offloading workloads to the CPU is counterproductive for accelerated AI workloads and does not optimize GPU resource sharing.