How much VRAM do I need for local AI?

Small 7B language models can run with 8GB to 12GB of VRAM when quantized. Larger 30B to 70B models usually need 24GB to 48GB or more depending on precision, context length and whether the workload is inference or fine-tuning.

Is GPU or CPU more important for AI workloads?

The GPU is usually the most important part for local AI because VRAM and CUDA or accelerator support determine what models can run efficiently. CPU and system RAM still matter for loading data, multitasking and large model offloading.

Can I run Stable Diffusion with 8GB VRAM?

Yes, many Stable Diffusion workflows can run on 8GB VRAM, especially with optimized settings. Higher resolutions, SDXL, ControlNet and batch generation are smoother with 12GB to 16GB or more.

Local AI build planner

Find the hardware your AI workload actually needs.

Estimate GPU VRAM, system RAM, storage, PSU wattage and budget for LLMs, image generation, fine-tuning and creator machines.

Start calculating View quick specs

Calculator

AI GPU, VRAM and PC build estimator

Choose your workload and model profile. Results update instantly.

Workload Model size Precision / quantization Usage intensity Budget range $2,200

Quick specs

Common AI hardware requirements

Workload VRAM System RAM Best fit

7B LLM inference 8-12GB 16-32GB Budget AI PC

13B LLM inference 12-16GB 32GB Midrange GPU

70B quantized LLM 40-48GB+ 64-128GB High-end workstation

Stable Diffusion XL 12-16GB 32GB Creator machine

Operating system

Linux or Windows for AI?

Linux is usually better for serious local AI work. Windows is easier for everyday PC use. WSL2 gives many builders the best middle ground.

Best for AI tooling Linux
Most open-source AI projects, CUDA workflows, Docker setups and server deployments are built with Linux first.
Best for beginners Windows
Windows is simpler if you also game, edit video, use Adobe apps or want familiar driver and desktop management.
Best practical setup Windows + WSL2
Run Windows as your main desktop, then use Ubuntu through WSL2 for Python, model tools and command-line AI projects.

FAQ

AI hardware questions

How accurate is this AI hardware calculator?

It gives planning estimates based on common VRAM, RAM and power requirements. Real results vary by model, context length, batch size, drivers and optimization settings.

Should I buy more VRAM or more system RAM?

Prioritize VRAM first for GPU-heavy AI workloads. Add system RAM when you plan to run larger models, multitask, use CPU offloading or maintain large datasets locally.

What is the easiest AI PC build to monetize with this site?

Start with AI GPU requirement pages, then add affiliate links to GPUs, prebuilt workstations and cloud GPU services once the pages get search traffic.