Guide · Local AI
Mac vs GPU for local AI
Answer:Buy a used RTX 3090 (~$950) if you want max tokens/sec and CUDA. Step up to a 5090 when 32GB VRAM is worth the bill. Buy a Mac mini M4 Pro 24GB+ (~$1,200) if you want quiet, low power, always-on inference. Need more than 24–32GB GPU memory? Prefer 64GB+ Mac Studio or a 48GB workstation GPU.
How the hardware differs
- Discrete GPU (NVIDIA/AMD): Fast matrix math, separate VRAM. Model must fit in VRAM (or spill slowly to system RAM).
- Apple Silicon: Unified memory — CPU + GPU share one pool. Large models load more easily; raw tokens/sec often lower than a 4090.
When Mac wins
- High memory without buying a $3k+ workstation card (64GB / 128GB Studio configs)
- Desk-friendly power and noise (often tens of watts at idle/load vs 300–450W GPUs)
- Simple always-on private assistant (Ollama, MLX, llama.cpp)
When GPU wins
- Maximum tokens/sec and batch throughput
- CUDA-only tools, many training/fine-tune stacks
- Upgrade path: swap one card instead of whole machine
- Gaming + AI on the same box
Fair-value anchors (editorial)
- RTX 3090 24GB — ~$950 used
- RTX 4090 24GB — ~$1,800 used
- Mac mini M4 Pro 24GB — ~$1,200
- Mac Studio M2 Max 64GB — ~$1,800 used
Practical recommendation
- Budget local AI, already have a PC: used 3090 or 16–24GB Ada card.
- Want one quiet appliance: Mac mini with ≥24GB; used M2 Pro 32GB is often the value pick.
- Huge models, no rack: used high-RAM Mac Studio or dual high-VRAM GPUs.
FAQ
Should I buy a Mac or a GPU for local AI?
Buy a used RTX 3090 (~$950) if you want max tokens/sec and CUDA. Buy a Mac mini M4 Pro 24GB+ (~$1,200) if you want quiet, low-power always-on inference. For models bigger than 24GB VRAM, prefer 64GB+ Mac Studio or a 48GB workstation GPU.