Why Use Kubernetes for Generative AI: Get Started with Hyperstack Kubernetes

Kubernetes has become the go-to platform for companies looking to scale their Generative AI applications. For instance, OpenAI primarily uses Kubernetes as a batch scheduling system and relies on an auto scaler to scale up and down their cluster dynamically. Christopher Berner, Head of Compute at OpenAI, says that this strategy not only reduces costs for idle nodes but also maintains low latency and enables rapid iteration [source]. This shows why Kubernetes is an ideal choice for managing and scaling Generative AI workloads in the cloud. Continue reading as we explore more about using K8s for Generative AI.

Why Use Kubernetes for GenAI and AI Cloud?

Here’s why Kubernetes is the ideal choice for Generative AI in the cloud:

1. Granular Scaling Capabilities

Generative AI models can be highly demanding regarding computational resources during various phases such as training, fine-tuning and inference. Using Kubernetes offers granular scaling capabilities to scale individual containers or entire clusters up or down based on the real-time demands of your Gen AI workloads. However, on-premises environments are often limited by their physical infrastructure making them less flexible. But the good part is that you can implement AI on Kubernetes in the cloud to take advantage of Kubernetes auto-scaling features. Cloud platforms provide virtually unlimited resources that allow Kubernetes to scale your applications without hardware limitations. 

2. Cost Efficiency 

One of Kubernetes’ strengths is its ability to optimise resource usage by efficiently distributing and scheduling workloads across available nodes. This is important for Generative AI, which can vary significantly in resource consumption depending on the models' complexity and the datasets' size. In an AI cloud environment, you only pay for the resources you use and Kubernetes helps you minimise costs by ensuring that resources are allocated efficiently. 

3. Flexibility 

Kubernetes for AI offers unmatched flexibility and agility so you can deploy, update and manage your Generative AI models with ease. AI Cloud environments provide a wide range of tools and services that integrate seamlessly with Kubernetes to build complex AI pipelines, automate workflows and deploy models across multiple regions or even between different cloud providers to prevent vendor lock-in. This flexibility enables you to find the most cost-effective solution by choosing from different types of GPUs and cloud providers.

4. Simplified Management 

We all know that managing Kubernetes clusters is complex, particularly when it comes to tasks like monitoring, updating and securing the environment. However, cloud providers like Hyperstack simplify this process by offering on-demand Kubernetes services that handle much of this complexity for you. The current on-demand Kubernetes product is still in beta but will be released soon to the public with features including automated deployment, CSI driver, auto-scaling and more!

How Hyperstack Supports Kubernetes Integration in the Cloud?

At Hyperstack, we aim to democratise AI by lowering the barriers to containerised solutions. That's why we're building On-Demand Kubernetes, a robust and AI-optimised Kubernetes service designed to simplify and accelerate your AI development.

Here’s how we’re optimising our cloud platform for Kubernetes:

1. Optimised VM Images for Docker: We’ve fine-tuned our VM images specifically for Dockers to ensure containers run efficiently with minimal overhead. This optimisation reduces startup times and resource consumption. So, it is easier to deploy and manage containerised AI workloads.
2. CSI Driver for Shared Storage: We’ve developed a Container Storage Interface (CSI) driver that enables shared storage across containers. This is useful for your Generative AI workloads, which often require access to large datasets or model files. With our CSI driver, you can easily share storage across multiple containers, improving data accessibility and reducing redundancy.
3. Single API Request to Launch Kubernetes Cluster: Deploying a Kubernetes cluster on Hyperstack is as simple as making a single API request. This request automatically provisions all the necessary components, including the master node, load balancer, bastion VM and worker nodes. This streamlined process reduces the complexity and time involved in setting up Kubernetes clusters, so you can focus on deploying your AI models.
4. Single API Request to Delete Kubernetes Cluster: Similarly, tearing down a Kubernetes cluster is just as easy with a single API request on Hyperstack. This ensures you can quickly decommission resources when they are no longer needed.
5. Autoscaling of Worker Nodes: To further improve the scalability of Kubernetes on Hyperstack, we are working on an auto-scaling feature. This will allow your cluster to automatically scale up or down based on demand so you have the right amount of computational power available for your Generative AI workloads.

Be Among the First to Experience Hyperstack’s On-Demand Kubernetes! 

Enjoy complimentary access to the Beta Version of Hyperstack's On-Demand Kubernetes and have a say on our product development. Apply now to get started!

FAQs

What is the main advantage of using Kubernetes for Generative AI?

Kubernetes offers dynamic scaling and efficient resource management, ideal for handling the intensive demands of Generative AI workloads.

How does Hyperstack simplify Kubernetes management for AI?

Hyperstack provides optimised VM images, a CSI driver for shared storage, and streamlined APIs for launching and managing Kubernetes clusters.

Can Kubernetes handle GPU-accelerated workloads for AI?