The original title is "Self-Hosted Ollama Homelab: 3 Mistakes Running Local LLMs"

Short summary

Running Ollama locally requires GPU compatibility verification—CUDA version mismatches silently cause CPU fallback, destroying inference speed. Resource limits are critical: set OLLAMA_NUM_PARALLEL=1 on constrained hosts and enforce Docker memory caps to prevent OOM kills of other services. Expose the API only to localhost or behind authentication, as default binding publishes port 11434 to all interfaces, creating security risk in networked homelabs.

• •GPU acceleration requires exact CUDA version match (11.8+)—mismatches silently degrade to CPU without warnings
• •Resource constraints prevent OOM cascades: use OLLAMA_NUM_PARALLEL=1, memory limits in Docker, and model eviction policies
• •Ollama API has no built-in auth—network exposure requires firewall rules or reverse-proxy authentication

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