This article addresses the issue of slow performance when running Large Language Models (LLMs) with Ollama on K3s, a lightweight Kubernetes distribution. The primary cause identified is that containerized applications within Kubernetes, by default, lack direct access to host hardware like NVIDIA GPUs, forcing Ollama to fall back to CPU processing. The solution involves setting up a three-layer bridge to enable GPU pass-through, which includes installing NVIDIA drivers and the NVIDIA Container Toolkit on the host, and configuring the NVIDIA K8s Device Plugin to expose GPU resources to the K3s scheduler. AI
IMPACT Enables faster local LLM inference by properly configuring GPU acceleration within containerized Kubernetes environments.
RANK_REASON The article provides a technical guide for optimizing the performance of an existing tool (Ollama) on a specific infrastructure (K3s).
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