How-To
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How to Run Continue with a Local Model Using Ollama
Install Ollama, pull a code model, and connect it to Continue so your AI assistant runs fully offline.
How to Install Ollama on macOS
Get Ollama running on a Mac in under 10 minutes: install via Homebrew or the native app, pull your first model, and confirm the local server is responding.
How to Install Ollama on Windows
Download the Windows installer, confirm PATH is set, verify the local API server is up, and understand when WSL2 gives you better GPU performance.
How to Run Llama 3 Locally with Ollama
Pull the Llama 3.3 model, run an interactive chat session, switch between the 8B and 70B variants, and understand the VRAM requirements for each size.
How to Use Ollama as a Drop-In OpenAI API
Point any OpenAI-compatible SDK or tool at your local Ollama server by changing one URL, and use OLLAMA_HOST to expose it on your network.
How to Run Mistral 7B Locally
Pull Mistral 7B with Ollama, understand when to use mistral-nemo instead, check your VRAM requirement, and tune a system prompt to get sharper answers.
How to Set Up Open WebUI for Local AI
Run Open WebUI in Docker, connect it to Ollama, switch between models in one click, and create custom personas that save your best system prompts.
How to Fine-Tune Llama 3 on Your Own Data with Unsloth
Install Unsloth, load Llama 3, format your JSONL dataset, run a LoRA training job, and export the result as a GGUF file ready for Ollama.
How to Run Your Fine-Tuned Model in Ollama
Take the GGUF file from your fine-tune, write a Modelfile, register it with Ollama, run it locally, and optionally push it to the Ollama registry.
How to Run OpenAI's gpt-oss-20b Locally with Ollama
gpt-oss-20b is OpenAI's first open-weight model under Apache 2.0, and it runs on a 16 GB machine. Here is the exact, step-by-step setup with Ollama, from install to your first chat and API call.
Best GGUF Models to Run by VRAM Tier (8GB, 12GB, 16GB, 24GB, 48GB)
A current, no-nonsense map of which local models actually fit your GPU. Pick a VRAM tier, get the right GGUF model and the exact ollama pull command, plus the quant and context math that decides whether it loads or crashes.
Replace GitHub Copilot With a Local Coding Model (Qwen2.5-Coder + Continue.dev)
Drop the $10–19/month subscription and run your own coding assistant inside VS Code. This is the exact setup: Ollama plus Qwen2.5-Coder for chat and tab-autocomplete through the free Continue.dev extension, with honest notes on what you give up.
Run DeepSeek V4 Locally: Which Size Actually Fits Your Hardware (Flash vs Pro)
DeepSeek V4 is a frontier open-weight release, but the headline size and the memory you actually need are not the same number. Here is what fits a single consumer GPU, what needs a workstation, and the realistic fallback for normal hardware.
Run LLMs in Your Browser With WebGPU: No Install, No Server (WebLLM)
Try local AI before you commit to Ollama or buy a GPU. WebLLM runs a real quantized model entirely inside a browser tab using WebGPU, with nothing sent to a server. This is the zero-install path, the model size limits, and the minimal code to embed it in your own page.
How to Use Ollama Local Models in Dify
Point a self-hosted Dify at a local Ollama server, register a model like Llama 3.1 or Qwen, and run your apps fully offline with no per-token cost.