ACI provides a fast and simple way to run containers in the cloud. As a serverless solution, ACI eliminates the need to manage underlying infrastructure, automatically scaling to meet application demands. Customers benefit from using ACI because it offers flexible resource allocation, pay-per-use pricing, and rapid deployment, making it easier to focus on development and innovation without worrying about infrastructure management. 

Today, we are excited to announce the public preview of larger container sizes on Azure Container Instances (ACI). Customers can now deploy workloads with higher vCPU and memory for standard containers, confidential containers, containers with virtual networks, and containers utilizing virtual nodes to connect to Azure Kubernetes Service (AKS). ACI now supports vCPU counts greater than 4 and memory capacities of 16 GB, with a maximum of 32 vCPU and 256 GB for standard containers and a maximum of 32 vCPU and 192 GB for confidential containers. 

Benefits of Larger Container Sizes on ACI

Enhanced Performance 

More vCPUs mean more processing power, allowing for more efficient handling of complex tasks and applications. The enhanced performance from more vCPUs and larger GB capacity offers faster processing times and reduced latency, which can translate to cost savings in terms of time and productivity. Larger container groups with more GB can handle bigger datasets and more extensive workloads, making them ideal for data-intensive applications. 

Simplified Scalability 

Larger container groups provide the flexibility to scale up resources even higher as needed, accommodating growing business demands without compromising performance. Larger container SKUs can simplify the scaling process. Instead of managing many smaller containers, you can scale your applications with fewer, larger ones, potentially reducing the need for frequent scaling adjustments. 

Scenarios for Larger Container Sizes 

Data Inferencing 

Larger container SKUs are ideal for data inferencing tasks that require robust computational power. Examples include real-time fraud detection in financial transactions, predictive maintenance in manufacturing, and personalized recommendation engines in e-commerce. These containers ensure efficient and secure processing of large datasets for accurate predictions and insights. 

Collaborative Analytics 

When multiple parties need to share and analyze data, larger container SKUs provide a secure and efficient solution. For instance, companies in healthcare can collaborate on patient data analytics while maintaining confidentiality. Similarly, research institutions can share large datasets for scientific studies without compromising data privacy. 

Big Data Processing 

Organizations dealing with large-scale data processing can benefit from the enhanced capacity of larger container SKUs. Examples include processing customer data for targeted marketing campaigns, analyzing social media trends for sentiment analysis, and conducting large-scale financial modeling for risk assessment. These containers ensure efficient handling of extensive workloads. 

High-Performance Computing 

High-performance computing applications, such as climate modeling, genomic research, and computational fluid dynamics, demand substantial computational power. Larger container SKUs provide the necessary resources to support these intensive tasks, enabling precise simulations and faster results. 

How to start using Larger Container Sizes 

To begin using Larger Container Sizes, follow these steps. 

1. If you plan to run containers larger than 4 vCPU and 16 GB, you must request quota. 

1. Once your quota has been allocated, you can deploy your container groups through Azure portal, Azure CLI, PowerShell, ARM template, or any other method that allows you to connect to your container groups in Azure.  

Here are some tutorials for how to deploy containers using different methods. 

1. Quickstart - Deploy Docker container to container instance - Azure CLI - Azure Container Instances | Microsoft Learn
2. Quickstart - Deploy Docker container to container instance - Portal - Azure Container Instances | Microsoft Learn
3. Quickstart - Deploy Docker container to container instance - PowerShell - Azure Container Instances | Microsoft Learn
4. Quickstart - create a container instance - Bicep - Azure Container Instances | Microsoft Learn
5. Quickstart - Create a container instance - Azure Resource Manager template - Azure Container Instances | Microsoft Learn

To learn more about Azure Container Instances, see Serverless containers in Azure - Azure Container Instances | Microsoft Learn.