Blog · Platforms · September 20, 2026 · 7 min read
Local AI on Apple Silicon: How Far Can 16GB, 36GB, or 128GB of Unified Memory Go?
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Macs play by different rules than Windows PCs. There is no separate graphics card — the CPU and GPU share one pool of unified memory — so the question “how much VRAM do I have?” becomes “how much RAM did I buy, and how much can the GPU borrow?” By default the GPU can address roughly 65–75% of total memory, configurable higher on higher-end machines.
What each memory tier realistically runs
| Unified memory | Usable for models | Comfortably runs (Q4) |
|---|---|---|
| 16 GB | ~10–12 GB | 7–9B models at Q4/Q5; 14B tight |
| 24–36 GB | ~16–27 GB | 14B at Q6, 27–32B at Q4, small MoE models |
| 48–64 GB | ~32–48 GB | 32B at Q6/Q8, 70B at Q3/Q4 |
| 96–128 GB | ~64–96 GB | 70B comfortably at Q4+, MoE flagships |
The bandwidth truth
Token generation speed is memory-bandwidth-bound, and Apple’s unified memory has a fixed ceiling per chip: roughly 273 GB/s on an M4 Pro and 546 GB/s on an M4 Max — excellent, but below an RTX 4090’s 1008 GB/s. In practice an M4 Max generates mid-sized models at very usable speeds, while huge models feel slower than on a comparable desktop rig. Our site treats Apple unified memory as GPU memory in the Hardware panel — select an Apple entry and the fit badges and speed estimates apply as-is.
Where Macs shine — and where they do not
- Shine: silence and efficiency. A Mac Studio pulling 60–90 W under load runs 70B-class models in a fan-quiet box on your desk — no 450 W furnace, no dedicated PSU upgrade.
- Shine: big memory available in one pool. 128 GB unified memory costs far less than 128 GB of enterprise GPU VRAM.
- Limit: sustained throughput. Desktop GPUs with double the bandwidth finish long generations faster; Apple wins on watts-per-token, not tokens-per-second-per-dollar.
- Limit: some tooling is CUDA-first. Ollama and llama.cpp are excellent on Apple Silicon (Metal acceleration), but cutting-edge inference servers sometimes lag.
Practical advice
If you are buying a Mac primarily for local AI, memory is the spec that matters — upgrade RAM at purchase time, because it is unchangeable later. As a rule of thumb: 16 GB Macs are great assistants for small models, 36–64 GB hits the sweet spot for 30B-class work, and 96–128 GB machines are genuine 70B workhorses. Whichever you own, select it in the Hardware panel and the whole library gets sized against your exact memory.