Cubes, Pipes, Power, and GPUs: Building a Cloud for AI

What is AI Cloud?

An AI Cloud is a specialized cloud computing environment tailored to meet the unique requirements of AI workloads. Unlike traditional clouds that handle general-purpose computing tasks, AI Clouds are optimized for the challenges of AI, including training, tuning, and inference. These clouds provide the necessary computational power, storage solutions, and software stacks to support the development and execution of AI workloads. They aim to be cost-effective, democratizing access to expensive computational power.

An effective AI Cloud should offer a comprehensive suite of features essential for AI development and deployment. First, it must provide high-performance computing resources, such as Graphics Processing Units (GPUs), which are critical for efficiently training and running AI models. Additionally, it should offer scalable storage solutions to manage the vast datasets AI applications require, along with a low-latency network environment to ensure smooth deployment and operation of AI workloads. Robust security measures are also essential to protect data and intellectual property from potential threats.

Why Build an AI Cloud?

AI Clouds provide the computational power necessary to leverage the latest AI models and tools, allowing companies to adopt AI technology without making substantial hardware investments. Optimized allocation of costly resources, like GPUs, leads to greater cost efficiencies, reducing the overall expenses associated with AI development.

With AI technology expanding rapidly across all industry sectors, the market for AI solutions is expected to grow significantly, with projections indicating revenue in the billions in the coming years. The high demand for GPU resources further underscores the opportunity for independent Cloud Service Providers (CSPs) to launch specialized AI Cloud services.

How to Build an AI Cloud

As a company that has been building cloud-native infrastructures from the ground up, we are eager to share insights gained from our extensive research and hands-on experience in constructing AI Clouds. Broadly, the technological stack can be abstracted into three main layers:

• Infrastructure Layer

• Orchestration Layer

• Platform Layer

Infrastructure: Compute, GPU, Network, Storage

The infrastructure layer comprises the physical hardware, including high-performance servers equipped with GPUs, TPUs, NPUs, and other hardware accelerators. These components provide the computational power required for AI workloads. Hardware accelerators are at the core of AI processing and are key to differentiating performance levels. Various GPUs, catering to different performance needs, should be pooled and managed through orchestration layers and specialized drivers to maximize efficiency. NVIDIA GPUs remain the most common choice, leveraging its first-mover advantage in specialized AI chips.

High-speed networks and storage solutions are essential for handling large datasets and model artifacts. Both block and object storage solutions are needed to accommodate the diverse storage requirements of AI applications.

Orchestration: Kubernetes

The orchestration layer, managed via Kubernetes, ensures efficient resource utilization across AI Cloud users. Kubernetes’ flexibility makes it an ideal choice for developing, training, and deploying AI workloads, including Large Language Models (LLMs). It provides a standardized approach to running computing workloads, eliminating the need for outdated and inefficient tools. Kubernetes can be used at every stage of the LLM lifecycle—from data preparation and pre-training to fine-tuning and inference.

Kubernetes also enables GPU pooling while ensuring isolation and sharing between tenants. It increases GPU utilization, reduces costs, and allows multiple workloads to access GPUs. However, implementing this requires careful orchestration and resource management, especially when dynamically allocating and deallocating resources.

The Platform Layer

Kubernetes also provides a standardized set of APIs for MLOps. A cloud-native approach is already common among MLOps teams, offering support for microservices, declarative APIs, composability, portability, and automation. Kubeflow is one such tool that supports AI and ML operations within this framework.

A typical MLOps pipeline includes:

• Data Preparation: Collection, cleaning, pre-processing, and feature engineering.

• Model Training: Model selection, architecture design, and hyperparameter tuning.

• CI/CD and Model Registry: Storage and management.

• Model Serving: Deployment and inference.

• Observability: Monitoring usage, load, model drift, and ensuring security.

The platform layer is responsible for building these pipelines. The Cloud Native Computing Foundation (CNCF) is actively working to integrate and harmonize current AI tools into the cloud-native landscape.

Challenges and Design Principles

Building an AI Cloud presents several challenges. Scalability is a major concern, as the infrastructure must handle varying demands. Cost management is another critical issue, requiring a balance between performance and efficiency to ensure cost-effective operations. Data privacy must also be maintained with strict compliance to regulatory requirements to protect sensitive information. Ensuring interoperability with a range of AI tools and frameworks is essential as well.

Multi-tenancy is a key requirement when building an AI Cloud. Given that GPUs are expensive and valuable resources, an AI Cloud must allow for sharing and pooling while ensuring strong isolation, quotas, and limits for each user to maintain reliability and security.

Several design principles guide the construction of an AI Cloud:

• Modularity: Ensures components can be easily updated or replaced as technology evolves.

• Flexibility: Allows the architecture to adapt to new developments in AI.

• Efficiency: Optimizes resource use and enhances performance while minimizing costs.

• Security: Embeds safeguards at every layer to protect data and models.

Reference Architecture

The following reference architecture offers a high-level overview of the key components involved in building an AI Cloud infrastructure.

This is a simplified representation. For a more detailed discussion or tailored guidance, we encourage readers to schedule a meeting with our team of experts.

Call to Action

An AI Cloud infrastructure is essential for any organization looking to harness the power of AI. Building and managing such infrastructure requires robust hardware optimized for AI workloads and sophisticated management and orchestration tools. Key elements include secure tenant isolation, automation, and dynamic configuration.

The larger hyperscalers are experts in this field, employing proprietary software and leveraging two decades of experience to offer customer-friendly APIs and autonomously manage complex AI setups with dynamic, error-free configurations.

However, building these advanced cloud abstraction layers is a complex and resource-intensive task, often requiring years of dedicated development by specialized cloud infrastructure teams. Independent CSPs focusing solely on the hardware layer frequently struggle to navigate the cloud-native ecosystem.

At Clastix, we have made it our mission to build efficient cloud infrastructures. Over the past few years, we have developed a knowledge base and built open-source tools like Capsule and Kamaji, specifically designed to enhance scalability, efficiency, and security in modern cloud infrastructures. We invite you to engage with us for a consultation to explore how we can help you design and implement your AI Cloud service, reducing time to market and keeping you ahead of the competition.