Local AI How-To Guides
Run AI models on your own hardware. These guides cover Ollama and LM Studio setup, picking a model that fits your VRAM, quantization, and getting usable speed.
Run Models
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 Pick the Right Local AI Model for Your Hardware
Match your GPU VRAM tier to concrete model recommendations, understand Q4/Q5/Q8 quantization, and use a simple rule of thumb to avoid out-of-memory crashes before you download.
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.
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 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.
GGUF vs MLX vs NVFP4: Local AI Quantization Formats Explained
Three names keep showing up when you go to download a local model: GGUF, MLX, and the newer NVFP4. They are not interchangeable, and picking the wrong one wastes memory or leaves your hardware idle. Here is what each format is, which one your machine actually wants, and how to choose in ten seconds.
Local Apps & UIs
How to Set Up LM Studio for Local AI
Download LM Studio, grab a model from the built-in hub, start the local server, and have a working private AI chat running on your own hardware in about ten minutes.
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.
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 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.
Hardware & Builds
Best GPU for Running AI Locally in 2026
VRAM is the single spec that determines which models you can run. Here is how every major GPU stacks up for local LLMs and image generation in 2026.
How Much RAM Do You Need for Local AI?
RAM requirements for local AI depend on whether you are talking about GPU VRAM or system RAM, and which models you want to run. Here are the real numbers.
Mac vs PC for Local AI: Which Should You Choose?
Apple Silicon unified memory and PC discrete GPUs take completely different approaches to local AI. Here is what each does better and how to pick based on your workload.
How to Build a Local AI Workstation on Any Budget
Four budget tiers from $500 to $5,000+ with specific part recommendations and the real trade-offs at each level.
Model Spotlights
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.
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.
Local OCR and Document AI: Self-Host baidu Unlimited-OCR for Private Document Parsing
Invoices, receipts, contracts and scanned PDFs are exactly the documents you do not want to ship to a per-page cloud API. baidu Unlimited-OCR is a brand-new open-weight model that parses whole PDFs in one pass, on your own hardware. Here is what it is, what it runs on, and how to go from a scanned page to structured JSON.
gpt-oss-20b vs gpt-oss-120b: Which Open Model Should You Run?
OpenAI shipped two open-weight models. One runs on a laptop, the other needs a serious GPU. Here is exactly how they differ and which one fits your hardware and use case.
Fine-Tuning
How to Fine-Tune an LLM with LoRA (Beginner Guide)
Understand what fine-tuning actually does, why LoRA makes it possible on consumer hardware, and what you need before you write a single line of training code.
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 Prepare a Training Dataset for LLM Fine-Tuning
Build a clean JSONL dataset using the right conversation format, apply quality filters, hit the minimum example count, and use synthetic generation to fill gaps.
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.