Local AI on a 16 GB NVIDIA Graphics Card

If you have a 16 GB NVIDIA graphics card, you can run AI on your own computer without sending anything to the internet. Our machine has an RTX 5060 Ti; the video shows what is installed on it in a couple of minutes, and here I go a little deeper. For the wider picture, see the Agentic AI Guide.

Is a 16 GB card enough?

It is, just not for everything at once. A few NVIDIA cards with 16 GB of memory: RTX 5060 Ti 16 GB, RTX 5070 Ti, RTX 5080, RTX 4060 Ti 16 GB, RTX 4070 Ti SUPER, RTX 4080 and RTX 4080 SUPER. Same memory, not the same speed. That memory is enough for a chat model, a dictation model or a voice model, one at a time.

List of NVIDIA graphics cards with 16 GB of memory and a panel showing that no data is sent to the internet

Why NVIDIA? The real difference is CUDA

Speed isn’t the only reason. The real advantage is CUDA: the software layer NVIDIA built so a graphics card can do work beyond graphics. Most local AI tools target it first; install steps, ready-made packages and bug fixes show up there first. Some tools run on other cards too, but the road isn’t as clear. On our setup both the chat engine and the audio models run on CUDA.

Layer diagram: NVIDIA graphics card, CUDA on top of it, and tools such as llama.cpp, PyTorch and Whisper at the top

Qwen or Gemma?

Two model families are installed on the card. Qwen is smarter; I pick it for hard reasoning and careful writing. Gemma is faster and can also look at images; if you want a screenshot explained, you pick Gemma.

There are uncensored versions too. They aren’t smarter; they just turn down fewer requests. That doesn’t mean the answer is more accurate.

Qwen and Gemma compared: Qwen is smarter, Gemma is faster and reads images; uncensored versions refuse less but are not more accurate

Tokens and context, in plain words

A model doesn’t read text word by word; it reads it in small pieces, and those pieces are called tokens. A short word can be a single piece, a long word several. I measured it on this computer with Qwen and Gemma: “cat” is one piece, “uncharacteristically” is three (un, character, istically).

Token example: the word cat is one piece, uncharacteristically splits into un, character and istically

Context is the model’s desk. Your question, your document, the earlier conversation and the answer it writes all sit on one desk. What doesn’t fit, the model doesn’t see. The desk itself lives in the graphics card’s memory: on our setup Qwen almost fills the card, so its desk is smaller than Gemma’s. And a big desk doesn’t guarantee it reads every detail correctly.

Context shown as a desk: question, document, earlier chat and answer on the desk, with a page that doesn't fit falling off

Not only language models: dictation with Whisper

A 16 GB card isn’t only for chat models. My favorite local AI is Whisper large v3, which turns speech into text. I built a small Windows app around it: I press two keys and talk, Whisper transcribes on the same graphics card, and a shortcut pastes the text into the open window. Emails, notes, instructions for AI agents; I run my computer by voice.

Dictation flow: Ctrl+Win starts recording, Whisper transcribes on the same graphics card, text goes to the clipboard, Ctrl+B pastes it into the open window

Most importantly: my voice never leaves the computer. The recording and the transcription stay on this machine; the text placed on the clipboard isn’t saved to Windows clipboard history or the cloud clipboard either. Only the text I choose to send goes out.

Privacy split: the recording, transcription and clipboard stay on this computer; only the text you choose to send goes to the internet

Other local audio models

More audio models run on the same card. VoxCPM2 reads text aloud; give it a short voice sample and it can read in your own voice. Chatterbox is our backup voice model. ZipEnhancer cleans the noise out of a recording, and ERes2Net measures how close a cloned voice is to the real one.

Local audio models: VoxCPM2 text to speech and voice cloning, Chatterbox backup, ZipEnhancer noise cleaning, ERes2Net voice similarity

Pick the job first, then the model

All of these models share the same memory. Load a big chat model right after dictating and it slows down; using them one at a time is easier. Hard reasoning: Qwen. Long documents and images: Gemma. Speech to text: Whisper. Text to speech: VoxCPM2.

Job to model map: hard reasoning Qwen, long documents and images Gemma, speech to text Whisper, text to speech VoxCPM2; same memory, one at a time

If you want to learn local AI setups together, follow the channel.

Frequently Asked Questions

Is a 16 GB graphics card enough for local AI?

Enough, but not for everything at once. The same memory serves a chat model, a dictation model or a voice model in turn; load them all together and things slow down.

Speed isn’t the only reason. The real difference is CUDA: most local AI tools are prepared for it first, so setup steps and error fixes show up there first. Tools for other cards exist, but the path is less clear.

Qwen or Gemma?

Qwen is smarter, so I pick it for hard reasoning and careful writing. Gemma is faster and can look at images. Uncensored versions aren’t smarter; they simply refuse fewer requests, which doesn’t make the answers more accurate.

Does my voice leave the computer when I dictate?

No. Whisper large v3 turns speech into text on the same graphics card, so the recording and the transcription stay on the computer. Only the text I choose to send goes out.