Tech & PC
How much VRAM do you need to run AI locally?
VRAM, RAM and GPU needed for local LLMs (7B–70B), AI image and video generation on your computer.
Quick answer
Running AI on your own computer means no subscriptions, no rate limits and full privacy: your prompts and data never leave your machine. The catch is hardware. Unlike gaming, where the GPU chip speed matters most, local AI lives and dies by one number: VRAM, the memory on your graphics card. A model either fits in VRAM or it doesn’t — and when it doesn’t, it runs 10-50× slower or not at all.
VRAM needed (graphics card)
6–8 GB
- Reference GPU
- RTX 4060 / RTX 3060 12 GB
- System RAM
- 16 GB
- Storage
- NVMe SSD 1-2 TB (models take tens of GB)
Q4 quantized models also run on CPU only (slow) or on Apple Silicon Macs with 16 GB unified memory.
VRAM matters most: it determines which models you can load. Free software to start: Ollama and LM Studio for chat, ComfyUI for images and video. Data updated July 2026.
How it works
As a rule of thumb, a quantized language model needs roughly 0.6–1 GB of VRAM per billion parameters: a 8B model fits in 8 GB, a 32B model wants 24 GB, and 70B models need dual GPUs or a Mac with lots of unified memory. Image generation (SDXL, FLUX) sits between 8 and 24 GB, while local video generation is the heaviest workload of all. Pick your use case above to get the exact requirements — if the calculator points you to 24 GB, compare current 24 GB graphics cards before buying — and start with free software: Ollama or LM Studio for chat, ComfyUI for images. On a smaller budget, mini PC RAM tiers for AI explains what compact boxes can run.
Highlighted links go to Amazon.
Frequently asked questions
Can I run AI locally without a graphics card?+
Yes, but only small language models (up to 7-8B parameters, quantized) run acceptably on CPU — expect 3-10 words per second instead of 30-80 on a GPU. Image and video generation on CPU is impractical. An alternative is a Mac with Apple Silicon: the unified memory acts as VRAM, so a MacBook with 16-32 GB runs surprisingly large models.
Is 12 GB of VRAM enough for local AI in 2026?+
It’s a solid entry point: 13-14B language models run well, SDXL image generation works, and quantized FLUX is usable. What stays out of reach are 30B+ models at good quality and video generation. If you’re buying today and AI is the main goal, 16 GB is the sweet spot and 24 GB is the enthusiast threshold.
NVIDIA or AMD for local AI?+
NVIDIA is still the safe choice: virtually every AI tool supports CUDA out of the box. AMD support (ROCm) has improved a lot and works well with Ollama and llama.cpp, but you’ll hit more friction with image/video tools. If you don’t want to troubleshoot, choose NVIDIA; if you find a great deal on a 24 GB AMD card and mostly want LLM chat, it can be worth it.
How much disk space do local AI models take?+
More than you’d think: a quantized 8B model is 5-8 GB, a 70B is 40+ GB, and image models with their extras run 10-30 GB each. If you experiment with several models you’ll fill 500 GB quickly — that’s why we recommend a 1-2 TB NVMe SSD. Loading speed matters too: models load in seconds from NVMe versus minutes from a hard drive.
As an Amazon Associate I earn from qualifying purchases — the price does not change for you.