Running AI inference in Docker: GPU passthrough and resource limits

On November 10, 2026, DEV Community's AI section published a hands-on guide titled "Running AI inference in Docker: how to configure GPU passthrough and resource limits" (original headline: "在 Docker 里跑 AI 推理：GPU 直通与资源限制怎么配"). The article targets constrained-hardware setups and explains how containers reach the host's GPU through the NVIDIA Container Toolkit. The key idea is that the container image does not bundle the kernel driver at all; instead, the runtime injects the host's driver libraries and device nodes into the container at startup. The piece walks through registering the runtime with Docker and restarting the daemon so the configuration takes effect, and it ships a diagram (FIG 1) illustrating the setup.

On the mechanics, the article stresses that a container cannot talk to a PCIe device without driver support. The NVIDIA Container Toolkit supplies this by wrapping the standard runtime with a hardware-aware one, nvidia-container-runtime. It also frames the problem contextually: a leading reason AI workloads fail inside containers is the boundary between the container's isolated namespace and the host's hardware. Running inference on raw CPUs creates significant latency bottlenecks, while exposing a GPU or accelerator requires deliberate host-side setup rather than a default configuration.

Only this single article is available so far, and there are no further developments on this topic that can be verified from the material at hand. The discussion currently rests at the level of configuration guidance, with no follow-up coverage, community response, or changelog-style updates present in the reporting collected to date.