# How-to guides

> Step-by-step guides with copyable commands, expected output and troubleshooting. Each says what we have and have not tested.

Canonical: https://thelocalbench.com/how-to · Data as of 2026-10-12 · The Local Bench (thelocalbench.com), data as of 2026-10-12, CC BY 4.0

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- [Run your first local model with Ollama and Open WebUI](https://thelocalbench.com/how-to/first-local-model-ollama-open-webui.md): Beginner, about 30 min, sourced, not yet tested. Install Ollama, pull Gemma 3, check the local API answers on port 11434, and run Open WebUI in Docker on localhost:3000 so you can chat in a browser. Use local models and disable external connections for local-only inference.
- [Build llama.cpp and run a model with GPU offload](https://thelocalbench.com/how-to/llama-cpp-gpu-offload.md): Intermediate, about 45 min, partly tested. Clone and build llama.cpp with -DGGML_CUDA=ON, start llama-server with -hf and -ngl, check /health and the OpenAI-compatible chat endpoint, then tune -ngl, -c and -fa to fit your VRAM.
- [Connect a local model to a coding agent (Aider + Ollama)](https://thelocalbench.com/how-to/local-model-coding-agent-aider.md): Intermediate, about 30 min, sourced, not yet tested. Install Aider with aider-install, point it at Ollama with OLLAMA_API_BASE, pull a coding model such as qwen3-coder:30b, start Ollama with a larger context, and run aider --model ollama_chat/<model> inside a git repository.
- [Set up private document search (RAG) with Ollama and Open WebUI](https://thelocalbench.com/how-to/private-document-search-rag.md): Intermediate, about 40 min, sourced, not yet tested. Pull a local embedding model and a tool-capable chat model in Ollama, raise Ollama's context length, point Open WebUI's document embedding at Ollama, create a knowledge base, upload files, and query it with
- [Run a tiny local model on your Mac CPU](https://thelocalbench.com/how-to/tiny-model-cpu.md): Beginner, about 15 min, tested by us. Run the TinyStories 15M model with a pinned llama2.c runtime on macOS. Verify the download checksum, compile with Apple clang, and generate text on the CPU.

JSON: https://thelocalbench.com/api/v1/how-to.json