What Is a Container, Really? Five Years of GPU Infrastructure
The article examines the evolution of container technology in GPU infrastructure over five years, clarifying what containers actually are in practice—beyond the buzzword—and how they have shaped modern cloud and AI workloads.
Background
Docker containers have become the standard way to package and deploy software, especially in cloud and AI infrastructure. The article argues that developers misunderstand containers: they treat them like lightweight virtual machines, but containers are actually just Linux processes with strict resource isolation (cgroups, namespaces). This distinction matters for GPU workloads (AI/ML), where containers share the host kernel and GPU drivers — unlike VMs, which virtualize the full hardware stack. The piece draws on the author’s five years of building GPU infrastructure at Beam (a serverless GPU platform), explaining real-world failures caused by treating containers as VMs: GPU memory leaks, driver version mismatches, and kernel-level quirks that only make sense once you understand the underlying Linux process model. Key context: the container-vs-VM debate is a long-running tension in cloud computing, but it becomes critical with GPUs because NVIDIA’s container runtime hooks directly into the host kernel.