Mac vs PC for Local AI: Which Should You Choose?
Apple Silicon unified memory vs NVIDIA discrete GPU — the honest tradeoffs for running LLMs at home.
The Fundamental Difference
PC (NVIDIA/AMD GPU): Fast but limited. An RTX 5090 has 32 GB VRAM at 1,792 GB/s — blazing fast, but 32 GB is the ceiling. Models that don't fit simply won't run.
Mac (Apple Silicon): Slow but spacious. An M4 Max has up to 128 GB unified memory at ~546 GB/s — much slower bandwidth, but you can fit 70B models at full quality that no single PC GPU can touch.
It's a tradeoff between speed and capacity.
Head-to-Head Comparison
| Spec | RTX 5090 | M4 Max 128GB |
|---|---|---|
| Memory | 32 GB GDDR7 | 128 GB Unified |
| Bandwidth | 1,792 GB/s | 546 GB/s |
| 7B Q4 speed | ~512 tok/s | ~156 tok/s |
| 70B Q4 speed | ~51 tok/s | ~15 tok/s |
| 70B Q8 | Won't fit (72 GB) | ~8 tok/s (fits!) |
| Power draw | 575W | ~60W |
| Noise | Loud under load | Silent |
| Price | ~$2,000 (GPU only) | ~$3,500 (whole computer) |
When to Choose PC
- You want the fastest possible inference (3-4x faster than Mac)
- Your models fit in 24-32 GB (7B-32B at Q4)
- You also game or do creative work
- You're serving multiple users (vLLM + CUDA is more mature)
- Budget is tight (used RTX 3090 at $800 is hard to beat)
When to Choose Mac
- You need to run 70B+ models at high quality (Q6/Q8/FP16)
- Silence and power efficiency matter (60W vs 575W)
- You want a complete computer, not just a GPU
- You're already in the Apple ecosystem
- Portability matters (MacBook Pro with 96 GB runs 70B Q4)
The community wisdom: "If your models fit in 24 GB, get a PC. If they don't, get a Mac. Don't buy a Mac for 7B models."
References & Further Reading
- [1]SitePoint (2026). Mac vs PC for Local LLMs 2026
- [2]Hypereal (2026). Best Small Local LLMs for Laptops
- [3]James Rien (2026). Local LLM Hardware Guide: The RAM Crisis
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