To enhance AI model performance on a virtualized platform, what is the best practice?

Running multiple workloads within a single container can enhance AI model performance on a virtualized platform through better resource utilization and management. This approach allows for efficient sharing of resources, leading to reduced overhead compared to running multiple separate virtual machines. Since containers are lightweight and start quickly, they facilitate faster deployment of AI models and make scaling more efficient. Additionally, containers can share the host system's kernel, which reduces redundancy and improves performance compared to traditional virtualization methods.

In contrast, isolating workloads in separate virtual machines can lead to increased resource consumption due to the overhead associated with running multiple operating systems, which may hinder overall performance. Utilizing bare metal for all applications may offer raw performance benefits but lacks the flexibility and scalability that a virtualized environment provides. Limiting the number of active VMs typically aims at reducing resource contention but may not effectively utilize available resources for AI models, particularly when high availability and responsive performance are critical.

Consequently, running multiple workloads within a single container is considered a best practice for optimizing AI model performance on virtualized platforms by promoting efficiency and agility.