Ollama Optimizer

Optimize Ollama configuration based on system hardware analysis.

When to Use

Use this skill when the user asks to optimize Ollama, configure Ollama, speed up Ollama, fix Ollama running slow, set up a local LLM, tune inference speed, reduce memory usage, or select models that fit their GPU/RAM. The skill analyzes hardware (GPU, VRAM, RAM, CPU) and produces tailored recommendations.

Do not use for LM Studio, llama.cpp, vLLM, or hosted-API LLM providers (OpenAI, Anthropic) — those use different runtimes and tuning surfaces.

Safety Rules

• Phases 1 and 2 are read-only. They run scripts/detect_system.py, ollama --version, ollama list, and ollama ps, and they change nothing.
• An applied change is any write to the machine: an env-var write, a service or app restart, ollama pull, a Modelfile ollama create, or any sudo command. Before each applied change, show its exact command and wait for the user's yes for that change.
• Saving the guide to the path the user chose is not an applied change.
• If the user asks for recommendations only, apply nothing and deliver the guide. A recommendations-only request is not a decline.
• Back up each config location before the first write to it (Choose where the env vars go).
• Never delete a model, and never run ollama rm. List oversized models in the guide instead.
• Never set OLLAMA_HOST=0.0.0.0 unless the user asks for network access. If they ask, warn that it exposes the Ollama API to the network without authentication.