If you only use cloud AI (Claude, ChatGPT), any modern computer works fine. This guide is for people who want to run AI models locally — which requires specific hardware considerations. We cover Mac vs Windows, RAM requirements, and specific recommendations by budget.
The M-series chip's unified memory architecture makes it the best value for running large models locally. An M3 MacBook Pro with 36 GB RAM runs Llama 70B smoothly — something that would cost $3,000+ in a Windows GPU setup.
Claude, ChatGPT, Gemini, and Copilot run in a browser. Any laptop made in the last 4 years with 8 GB RAM and a decent internet connection handles them perfectly. No special hardware needed.
| Factor | 🍎 Mac (Apple Silicon) | 🪟 Windows (+ NVIDIA GPU) |
|---|---|---|
| Local model speed | ✅ Excellent (unified memory) | ✅ Excellent (dedicated VRAM) |
| Running 7B models | ✅ Any M-chip Mac (16 GB) | ✅ Any RTX 3060+ (8 GB VRAM) |
| Running 70B models | ✅ 36–48 GB unified RAM | ❌ Needs 2× A100 or H100 (~$30K+) |
| Cost for 70B capable | 💰 ~$2,500 (M3 Pro 36 GB) | 💰 $10,000–$30,000+ |
| Battery life | ✅ Excellent (12–18 hrs) | ⚠️ Poor during GPU tasks |
| CUDA ecosystem (PyTorch) | ⚠️ MPS backend (some gaps) | ✅ Full CUDA support |
| Ollama support | ✅ Native, very fast | ✅ Works via NVIDIA CUDA |
| Cloud AI (Claude/Copilot) | ✅ Same as any machine | ✅ Same as any machine |
| Price/performance for local AI | ✅ Best value overall | ⚠️ High GPU cost premium |
Apple's M-series chips use unified memory — the CPU and GPU share the same RAM pool. This is why a 36 GB M3 Pro beats most Windows machines at local AI: the full 36 GB is available to the model.
Windows with a dedicated NVIDIA GPU is the other serious option for local AI. The advantage: full CUDA support for PyTorch/TensorFlow development and fine-tuning. The limitation: VRAM is the bottleneck (separate from system RAM), and 70B models need massive VRAM.
If you're comfortable with Linux, Ubuntu + NVIDIA GPU is the most flexible setup for AI development. Full CUDA support, no licensing restrictions, best PyTorch performance. Same GPU recommendations as Windows apply. Ollama runs natively. The tradeoff is setup complexity — not recommended for beginners.