GPUFits
Mac vs GPU for LLM Inference: Unified Memory Explained
updated 2026-08-04 · verified 2026-08-04
Apple Silicon is the odd one out in local LLMs: no VRAM, no CUDA, no fans you can hear — and it can hold models that would take four NVIDIA cards. Here’s the honest comparison.
How unified memory changes the game
On a PC, the GPU has its own VRAM (24GB on a 4090) and the CPU has separate RAM. Models must fit in VRAM entirely, or you pay a brutal speed penalty copying layers over PCIe.
Apple Silicon puts one memory pool on the chip package, shared by CPU and GPU. A 96GB Mac Studio can hold a 74GB model in its entirety — no consumer NVIDIA card comes within 3× of that. The trade-off: that shared pool is LPDDR5, not GDDR6X, so bandwidth tops out lower.
The bandwidth ladder (all verified)
| Chip | Bandwidth | Max memory | Rough 8B Q4 speed |
|---|---|---|---|
| M4 | 120 GB/s | 32GB | ~15 tok/s |
| M4 Pro | 273 GB/s | 64GB | ~40 tok/s |
| M4 Max (16-core) | 546 GB/s | 128GB | ~80 tok/s |
| M3 Ultra | 819 GB/s | 96GB¹ | ~120 tok/s |
| RTX 4090 (GDDR6X) | 1,008 GB/s | 24GB | ~150 tok/s |
| RTX 5090 (GDDR7) | 1,792 GB/s | 32GB | ~265 tok/s |
¹ Apple discontinued the 256GB/512GB Mac Studio options in 2026 due to the DRAM shortage; 96GB is the current ceiling, and the base price moved from $3,999 to $5,299.
The 75% rule
macOS doesn’t let the GPU have everything. It reserves memory for the system, and the practical ceiling for GPU allocation is about 65–75% of physical RAM. Our tools model this as:
Usable VRAM = physical memory × 0.75 → a 48GB Mac mini has ~36GB usable, a 96GB Studio ~72GB.
This is why “96GB Mac” can’t actually hold a 96GB model.
Who should buy what
Buy a Mac if: you want a silent, sip-power box that holds huge models (70B at Q8, gpt-oss-120b, Qwen3-235B at low quants), you value “it just works” with Ollama/LM Studio/MLX, and you can accept ~10–15 tok/s on 70B models — fine for chat, painful for agents.
Buy NVIDIA if: speed per dollar matters, you run CUDA tooling (vLLM, training, SD), or your models fit in 24–32GB anyway. A used 3090 at $1,000 embarrasses everything Apple makes on price-performance for ≤32B models.
The MLX factor
Apple’s MLX framework is typically 10–20% faster than llama.cpp on the same Mac for LLM inference, and it’s now the default backend in several Mac frontends. If you go the Mac route, prefer MLX builds of your model — and note MLX uses its own quantization formats rather than GGUF.
FAQ
Can a Mac mini run LLMs well?
The M4 Pro Mac mini (48GB, 273 GB/s) is a great quiet box for models up to 32B at Q4 — expect ~40 tok/s on 8B models. The base M4 (120 GB/s) works for 7B–8B models but feels slow on anything bigger.
Why can't my Mac use all its RAM for the GPU?
macOS caps GPU memory allocation at roughly 65–75% of physical RAM (the rest is reserved for the OS). We model this as a 0.75 usable ratio — a 48GB Mac has about 36GB of usable VRAM.
Is a Mac Studio M3 Ultra faster than an RTX 4090 for LLMs?
No for speed, yes for capacity. The M3 Ultra's 819 GB/s is below the 4090's 1,008 GB/s, so per-token generation is slower. But its 96GB holds models (gpt-oss-120b, 70B at Q8) that no single 24GB card can touch.