Running AI on your own computer – not through a cloud API – used to mean setting up Python environments, wrestling with CUDA versions, and spending an afternoon reading error logs. In 2026, it's dramatically simpler. You can install a tool, run a command, and be chatting with a local model in under five minutes.

This guide walks through the whole process: what hardware you actually need, which software to install, which model to pick, and what to do after you've got it running. If you're new to local AI, this is the only guide you'll need.

Source note: install commands below are from Ollama's official docs; hardware rules link to our tested guides. Tested fit/speed notes are from an RX 6800M 12 GB laptop – see lab benchmarks. Model names change fast – if a tag below is retired, pick the closest current small model in the Ollama library at the same size.

What you need: hardware

The most important thing for local AI is memory – specifically, how much of the model fits in RAM or VRAM at once. The model's weights need to be loaded before it can run, and the quality of the model is roughly proportional to its size.

Here's a quick rule of thumb:

A GPU helps enormously, but it's not required. If you have a GPU, you need enough VRAM to hold the model. Run your numbers through the LLM VRAM Calculator to see what fits your card. If you're shopping for a GPU, the best GPU for local LLMs guide has the full breakdown.

For most people starting out, the simplest path is: use what you already have. Your existing laptop with 16 GB RAM can run a 7B model at conversational speed. If you decide you want more, then you can upgrade.

Step 1: Install Ollama

Ollama is the single best tool to start with. It wraps model downloading, quantization, and the inference engine into one command-line tool, and it works on Linux, macOS, and Windows. It handles GPU acceleration automatically when it detects an NVIDIA GPU, AMD GPU, or Apple Silicon.

On Linux/macOS: curl -fsSL https://ollama.com/install.sh | sh

On Windows: download the installer from ollama.com/download and run it.

That's it. After installation, Ollama runs as a background service and you can start pulling models. Ollama wraps llama.cpp, the engine underneath – see the guide when you want benchmarks or finer control.

Verify the install before you pull a big model: run ollama --version, then ollama ps (empty table = service healthy, nothing loaded yet). Check disk – budget ~5 GB per 7B Q4 model, ~10 GB per 14B. On Linux, rocm-smi or nvidia-smi confirms GPU visibility; on macOS, Activity Monitor shows the Ollama process. If the service will not start, reboot once – stale port 11434 locks cause half of first-install failures.

Step 2: Pick and download a model

Ollama has a large library of models you can pull by name. For a first-time setup, here are the best options based on your hardware:

If you're not sure, start with ollama pull llama3.2:3b – it's small, fast, and will run on almost anything. You can pull larger models later.

Size the model to your VRAM/RAM before you pull: a 7B Q4 needs ~5.5 GB total, a 14B Q4 ~10.5 GB – see how much VRAM you need and confirm with the LLM VRAM Calculator. Pulling a 70B tag on a 16 GB laptop wastes an hour of download for a model that will never load – the number-one beginner mistake.

Step 3: Chat with it

Once the model is downloaded, just run: 

ollama run llama3.2:3b

You'll get a terminal prompt where you can type messages. It's that simple. Type /bye to exit.

For a better experience, install a web UI. Ollama ships with a built-in API, and several community UIs will auto-detect it:

For tips on getting better answers, see the how to write better prompts guide.

What good looks like: a 3B model should feel instant on almost anything; a 7B Q4 on a modern CPU runs ~5–10 tok/s (readable, not instant); the same 7B on a modest GPU runs ~40+ tok/s. If your 7B crawls under 3 tok/s, you are offloading to RAM – drop a size or shorten context rather than blaming Ollama. Lab reference: 27B dense with partial offload managed 18–22 tok/s on a 12 GB card; 35B MoE held 25–29 tok/s at 262K – see lab benchmarks.

Step 4: What to do next

Once you have a local model running, the real fun starts. Here's what you can do with it:

Privacy checklist (do this once)

Local means private by default, but verify it: after your first chat, disconnect the network and keep chatting – if it still answers, nothing leaves the machine. Turn off any UI telemetry or crash reporting in Open WebUI settings, keep documents you index for RAG inside a local folder (not a synced cloud drive), and remember that pasting secrets into a local model is still pasting secrets into a log file – Ollama keeps history on disk. For sensitive work, this offline test plus local-only storage is the whole point of running your own stack instead of a cloud chatbot.

On AMD laptops/desktops, prefer the ROCm build path when Ollama offers it and fall back to Vulkan if decode stutters – backend choice moved results ±20–45% in our lab runs. Full driver steps are in the ROCm installation guide and the ROCm vs Vulkan guide.

If you hit problems

Most issues come down to memory. If the model doesn't start or runs very slowly:

For more detailed help, the Ollama GitHub repo has excellent documentation.

Bottom line

Local AI in 2026 is simple: install Ollama, pull a model, and start chatting. You don't need a $3,000 GPU or a degree in machine learning. A 16 GB laptop with a 7B model is enough to feel the real power of local AI – privacy, no subscription, no limits.

Once you're comfortable, explore larger models, try a GPU, or build a dedicated local AI machine using the DGX Spark, RTX Spark, or a Ryzen AI Max mini PC. The door is open, and it costs almost nothing to walk through.

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