The biggest myth that keeps people from creating is “my PC is not good enough.” For vibe coding — websites, software, AI apps — it almost certainly is. The serious hardware only matters when you want to run or train large AI models on your own machine, and even then the cloud is often the smarter move. Here is the honest breakdown.
From a basic laptop to a training rig — pick the lowest tier that fits what you want to build.
8GB RAM · any CPU · stable-ish internet
To vibecode websites and software you mostly need a browser, an editor, and a cloud AI (Claude Code, Cursor, or a hosted model). The heavy thinking happens on the AI's servers, not yours. A modest laptop — even a borrowed or used one — is enough to build and deploy real things.
16GB RAM · SSD · a recent CPU
More RAM and an SSD make multitasking, Docker, and bigger projects smooth. This is the sweet spot for most builders: fast enough for serious software and web work, and able to run small local AI models when you want to experiment offline.
NVIDIA 24GB+ VRAM (e.g. 3090/4090) · or Apple Silicon 32–64GB
You only need this if you want to run large models locally or fine-tune your own. VRAM (not just RAM) is what matters for AI. This is a serious investment — and for most people, renting a cloud GPU is smarter than buying one.
Rent, don't buy
Google Colab, cloud GPU providers, and hosted model APIs let you run and even train models without owning a graphics card. You pay per hour or per token instead of dropping a fortune on hardware that ages fast. For nearly everyone starting out, this is the right call.
Power, data, and cost are real constraints. Here is how builders work around them.
Keep a charged power bank or a small UPS/inverter, and turn on auto-save and Git commits often so an outage never costs you work. Cloud-based tools also mean your project lives online, not just on a machine that might lose power mid-build.
Download tools and model files once on good internet, then work offline where you can. Prefer tools with offline modes, and avoid re-downloading large dependencies. A cheap second SIM as backup internet saves a lot of frustration.
A high-end GPU can cost more than months of cloud rental you may never fully use. Start with what you have, rent compute when you need it, and only buy hardware once a real project demands it. Buying used can stretch your budget — just test before you pay.