12GB–16GB VRAM: Serious Local AI Without a Supercomputer

Somewhere around 12GB of VRAM, local AI stops feeling like a compromise. This is the tier where models start matching a lot of what you'd get from a paid cloud AI subscription — strong reasoning, solid coding help, and enough room to keep longer conversations going without running out of memory.

If you own an RTX 3060 (12GB), RTX 4070, RTX 4070 Ti, RTX 4080, or an AMD RX 7700/7800 XT, this guide is for you. As always, no coding experience required.

Is your computer in this tier?

Step 1: Install Ollama

Windows:

1. Go to ollama.com/download, click Download for Windows.
2. Run the installer file that downloads and click Install — accept the defaults.
3. Ollama now runs in the background automatically (look for the icon in your system tray).

Mac:

1. Go to ollama.com/download, click Download for macOS.
2. Unzip it, drag Ollama into Applications, and open it once.
3. Approve the command-line helper install prompt.

Linux:

Verify: open Terminal/Command Prompt and type:

Step 2: A quick note on quantization (in plain English)

You'll sometimes see model names with things like q4_K_M or q5_K_M after them. This just means "how compressed the model is." Lower numbers (like q4) use less memory but lose a tiny bit of quality; higher numbers (like q8) use more memory for slightly better output. Ollama's default download for each model is usually already a great, sensible balance (q4_K_M) — you don't need to touch this unless you want to experiment later.

Step 3: Install and run models for your tier

Microsoft Phi (best overall model at 8GB VRAM, still shines here)

Phi-4 (14B) punches well above its weight class — strong at math and logic while staying light enough to leave headroom for longer conversations.

Alibaba Qwen (best all-round coding & tool-use model)

Qwen3 14B is an excellent daily-driver for coding, writing, and general assistant tasks, and handles longer back-and-forth conversations well.

Google Gemma (best for reasoning + documents + images)

Gemma 3 12B can also understand images if you attach one in a compatible chat frontend, alongside strong general reasoning and long-context handling.

Meta Llama (reliable, widely-supported, great community support)

If you want extra headroom for a very long context window instead of a bigger model, sticking with Llama 3.1 8B (and increasing its context length — see Step 4 below) is a very solid choice at this tier too.

Mistral (fast, clean instruction-following)

Mistral Small is tuned for following instructions precisely — good for structured tasks like filling templates or extracting data from text.

DeepSeek (best for deep, step-by-step reasoning)

This is a distilled version of DeepSeek's reasoning model, small enough to run smoothly here while still showing genuinely strong step-by-step logical reasoning — great for math, puzzles, and debugging tricky code.

Step 4: Unlock longer conversations (optional, still easy)

At this tier you have enough VRAM to give models a longer "memory" (called a context window) for longer chats or documents. Before running a model, set this in your Terminal:

Windows (Command Prompt):

Mac/Linux (Terminal):

Then run your model as usual (e.g. ollama run qwen3:14b). This lets the model "remember" more of your conversation or read longer pasted documents. If you don't do this step, everything still works fine with the default settings — this is just an optional upgrade.

Step 5: Chat, or connect it to real tools

At this tier, models are reliable enough to use for real work, not just chatting:

• VS Code coding assistant: install the free Continue extension in VS Code and point it at http://localhost:11434 — Ollama's local address — to get AI code completion and chat directly in your editor, all running locally.
• A ChatGPT-style browser interface: install Open WebUI for a polished, familiar chat window with saved conversation history.

Tips for this tier

• Qwen3 14B or Phi-4 are the best "just pick one and go" defaults if you're not sure which model to start with.
• Use DeepSeek specifically for problems that need reasoning (math, logic, debugging) — for quick everyday chat, a non-reasoning model like Llama or Mistral will feel faster.
• Don't run two 14B models at once — you'll run out of VRAM. Close one before starting another.
• If a model tag says "not found" when you run it, double check the exact spelling on Ollama's model library page (ollama.com/library) — model names and version tags get updated over time.

Visual Roadmap

Lost in the steps above? Here's the whole process at a glance: