Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026
The best Mac for local LLMs is whichever one has the most unified memory you can afford: 16GB runs 8B models, 48GB handles 32B comfortably, and 70B-class models want 64GB+. This guide maps every RAM t
The best Mac for local LLMs is whichever one has the most unified memory you can afford: 16GB runs 8B models, 48GB handles 32B comfortably, and 70B-class models want 64GB+. This guide maps every RAM tier to what actually runs well, explains why memory bandwidth (not CPU speed) sets your tokens/sec, and gives concrete buy recommendations from $599 to $4,000.
Last Updated: July 2026
On a PC, your model must fit in GPU VRAM — and consumer cards top out at 24–32GB unless you spend thousands. Apple Silicon uses unified memory: the GPU shares the full system RAM pool. A $2,000 MacBook Pro with 48GB can load models that would require a $5,000+ multi-GPU rig on the PC side.
Two numbers decide everything when buying a Mac for local AI:
1. How much RAM — determines *which* models you can load at all. 2. Memory bandwidth (GB/s) — determines *how fast* they generate. LLM inference reads the entire model from memory for every token, so tokens/sec scales almost linearly with bandwidth.
CPU core count and Neural Engine specs are nearly irrelevant for LLM inference — don't pay for them.
macOS reserves a slice of unified memory for the system; by default the GPU can use roughly 65–75% of total RAM. A "16GB Mac" is really an ~11GB model budget, a "48GB Mac" is ~34GB. The table below already accounts for this.
| RAM | Usable for models | What runs well (Q4_K_M) | Verdict |
|---|---|---|---|
| 16GB | ~11 GB | 7–8B fast; 12–14B is the ceiling | Entry level — fine for chat & email |
| 24GB | ~17 GB | 14B comfortably; Gemma 3 27B just fits | Best budget sweet spot |
| 32GB | ~22 GB | 27B-class with room for context | Comfortable daily driver |
| 36GB | ~25 GB | 32B (Qwen, DeepSeek R1 distill) | Solid dev machine |
| 48GB | ~34 GB | 32B with big context; 70B Q3 (slow) | The enthusiast sweet spot |
| 64GB | ~45 GB | Llama 3.3 70B Q4 at usable speed | Serious local AI |
| 128GB | ~90 GB | 70B Q8, MoE giants (quantized), multiple models at once | Workstation class |
Two practical notes: quantization is what makes these fit (see Quantization Explained), and long context eats extra gigabytes on top of the weights — if you do RAG or long documents, buy one tier above your model size. You can sanity-check any specific model against any Mac in the hardware analyzer.
Within a chip generation, the tier (base/Pro/Max) matters far more than the generation itself:
| Chip | Memory bandwidth | Realistic 8B Q4 speed |
|---|---|---|
| M4 (base) | 120 GB/s | ~20–25 t/s |
| M3 Pro | 150 GB/s | ~25–28 t/s |
| M2 Pro | 200 GB/s | ~30–35 t/s |
| M4 Pro | 273 GB/s | ~45–55 t/s |
| M2 Max / M3 Max (40-core) | 400 GB/s | ~60–70 t/s |
| M4 Max (40-core) | 546 GB/s | ~80–95 t/s |
| M2 Ultra | 800 GB/s | ~110–130 t/s |
Note the trap in Apple's lineup: M3 Pro (150 GB/s) is slower for LLMs than the older M2 Pro (200 GB/s) — Apple cut the memory bus that generation. And the base M4's 120 GB/s means a maxed-out base chip is never a good LLM buy: for local AI, a used M2 Max beats a new base M4 every time.
The formula behind those estimates: tokens/sec ≈ bandwidth × ~0.7 ÷ model size in GB. A 4.9GB 8B Q4 model on an M4 Pro: 273 × 0.7 ÷ 4.9 ≈ 39 t/s conservative, 45–55 in practice with optimized runtimes like MLX or llama.cpp Metal.
The 16GB base Mini works, but the 24GB configuration is the single best value in local AI today: it opens up the 14–27B class where models stop feeling like toys. Skip the 16GB unless the budget is truly hard.
The M4 Pro's 273 GB/s doubles the base chip's bandwidth, and 48GB unlocks 32B models with full context. This is the recommended configuration for developers who want a serious always-on local AI box — pair it with our Personal AI Home Server guide and it serves your phone and laptop too. If you need a laptop, the MacBook Pro M4 at 32GB is the portable equivalent for the 14–27B class.
546 GB/s bandwidth and 64GB+ RAM puts Llama 3.3 70B Q4 at genuinely usable speed (~12–18 t/s) and 128GB runs it at Q8 or hosts several models simultaneously. For a desk-bound machine, the Mac Studio M4 Max gives the same silicon as the MacBook Pro for less money.
A used M1 Max Mac Studio 64GB (400 GB/s) regularly sells for $1,100–1,400 and runs 70B Q4 — the cheapest legitimate 70B machine you can buy. Apple Silicon has no mining-abuse history and unified memory can't degrade like a GPU's fans and thermal pads, which makes used Macs far lower-risk than used GPUs (compare with our used GPU guide).
A PC with an RTX 4090 (24GB, ~1,000 GB/s) generates 2–3x faster than any Mac *for models that fit in 24GB*. The Mac's advantage starts exactly where VRAM ends: at 32B+ models, the PC needs a second GPU (see Multi-GPU Inference) while the Mac just needs the RAM it already has — silently, at 40–65W. Speed per dollar: PC. Model size per dollar and per watt: Mac. Full comparison in the GPU Buyer's Guide.
16GB is the working minimum (8B models), 24–32GB is the value sweet spot (14–27B), 48GB covers the 32B class, and 64GB+ is where 70B models become practical. Buy one tier above the biggest model you plan to run so context and multitasking have room.
It runs 7–8B models acceptably (~20–25 t/s), which covers chat and writing. But its 120 GB/s bandwidth makes larger models feel slow regardless of RAM, so never buy a base chip with big RAM "for AI" — step up to the Pro instead.
Yes — a 64GB M4 Max runs Llama 3.3 70B Q4 at roughly 12–18 tokens/sec, which is comfortable reading speed. On 48GB it technically fits at Q3 but leaves little room for context; 64GB is the honest minimum for 70B work.
Bandwidth, not generation, drives LLM speed — and used/refurb prices on M-series Macs with high bandwidth (M2 Max, M1 Max) are already excellent. If a current config fits your model tier and budget, waiting rarely pays.