How to Configure GPU Passthrough, Memory Limits, and Auto-Scaling for AI Containers

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A technical guide published on DEV Community outlines best practices for deploying AI agents using container-native infrastructure with Docker and Kubernetes. The guide explains how NVIDIA's Container Toolkit enables GPU passthrough, allowing containerized agents to access physical GPUs with near-native performance for model inference. It also covers enforcing memory and CPU limits via Linux cgroups to prevent resource contention between agents running on the same host. For multi-agent deployments, the guide recommends using environment variables to assign specific GPUs to specific containers, improving predictability and isolation. Auto-scaling strategies are presented as essential for handling variable workloads while keeping infrastructure costs efficient.

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