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Apple Silicon for Local AI
Apple silicon wins local AI on bandwidth. The Mac Studio M3 Ultra runs at 819 GB/s — roughly 3× any Strix Halo or GB10 box — so it produces more tokens per second on the same model, and the M4 Max reaches up to 546 GB/s. The trade-off is CUDA: Apple runs MLX and llama.cpp only, and many AI tools still assume NVIDIA. The lineup scales from the entry Mac Mini M4 (120 GB/s) up to the M3 Ultra.
Top Picks
Apple Mac Studio M3 Ultra
$3,999 (96GB)
- Chip: Apple M3 Ultra (28-core CPU / 60-core GPU, up to 32/80)
- Neural Engine: 32-core
- Unified Memory: 96GB new (256/512GB configs discontinued 2026)
Apple Mac Studio M4 Max
$1,999 – $5,999
- Chip: Apple M4 Max
- CPU Cores: 16-core
- GPU Cores: 40-core
Apple Mac Mini M4 (base)
$499 – $799
- Chip: Apple M4 (10-core CPU / 10-core GPU)
- Neural Engine: 16-core
- Unified Memory: 16GB – 24GB
Apple Mac Mini M4 Pro
$1,399 – $1,599
- Chip: Apple M4 Pro
- CPU Cores: 12-core
- GPU Cores: 18-core
Comparisons
Models That Run on These Boxes
Llama 3.3 70B Instruct
Meta's flagship dense chat model — strong general reasoning, coding, and instruction-following that rivals much larger models. The default pick when a single big box has the memory to spare.
LlamaLlama 4 Scout
A mixture-of-experts model — only a fraction of its 109B total parameters are active per token, so it loads like a large model but runs far faster than its size suggests. A natural fit for unified-memory boxes with a long context window.
Qwen3Qwen3 8B
A capable small chat-and-reasoning model that fits comfortably even on budget boxes — a good everyday assistant when you don't need frontier-level depth.
Qwen3Qwen3 14B
The mid-size Qwen3 — noticeably stronger reasoning and coding than the 8B while still fitting mid-range hardware at Q4.
Qwen3Qwen3 32B
Qwen3's large dense model — near-flagship quality that still runs on a single unified-memory box, a strong local alternative to 70B-class models at lower memory cost.
DeepSeek-R1DeepSeek-R1 Distill Qwen 7B
R1's chain-of-thought reasoning distilled into a 7B Qwen base — the lightest way to get R1-style step-by-step reasoning locally, small enough for budget boxes.
DeepSeek-R1DeepSeek-R1 Distill Qwen 14B
The 14B R1 distill — a good balance of reasoning depth and hardware cost, running comfortably on mid-range unified-memory boxes.
DeepSeek-R1DeepSeek-R1 Distill Qwen 32B
The strongest Qwen-based R1 distill — heavy reasoning that still fits a single large-memory box, the sweet spot for local R1-style work.
DeepSeek-R1DeepSeek-R1 Distill Llama 70B
R1's reasoning distilled onto a Llama 70B base — the highest-quality distill, and the one to reach for when the full 671B R1 won't fit (it never does locally).
Gemma 3Gemma 3 4B
Google's smallest current Gemma — tiny memory footprint and multimodal, ideal for the most memory-constrained budget boxes and always-on assistants.
Gemma 3Gemma 3 12B
The mid-size Gemma 3 — solid general-purpose quality with a long context window, fitting mid-range hardware at Q4.
Gemma 3Gemma 3 27B
The largest Gemma 3 — frontier-adjacent quality for a dense open model, comfortably runnable on a single large-memory box.
GPT-OSSGPT-OSS 20B
OpenAI's small open-weight MoE — its 21B total parameters load like a mid-size model, but because only a fraction are active per token it runs fast on modest hardware.
GPT-OSSGPT-OSS 120B
OpenAI's large open-weight MoE — its 117B total parameters fit a single high-memory box, and because only a fraction are active per token it runs far quicker than its total suggests.
PhiPhi-4
Microsoft's 14B model punches well above its size on math and reasoning — a compact, mid-range-friendly pick when you want strong reasoning without a big memory bill.
MistralMistral Small 3.2 24B
Mistral's 24B dense model — fast, capable, and low-latency for its class, sitting neatly between the mid-size and large boxes.
Related Articles
Mac Studio M4 Max vs Strix Halo: Which 128GB Box Actually Runs Your Local LLM Faster?
Tom's Hardware measured the M4 Max at roughly 1.6× a Strix Halo box on tokens/sec. But a 128GB Strix Halo machine starts at $1,999. Here's the bandwidth math, the per-GB math, and the prefill caveat that decides which one you should actually buy.
ReadGuideUnified Memory vs VRAM for Local AI: Why Capacity Now Beats Bandwidth (2026)
VRAM is fast but capped at ~32GB; unified memory trades bandwidth for 128GB+ of capacity. Here's the exact trade-off, why Mixture-of-Experts models flipped the math, and which box to buy for the model you actually run.
ReadGuideThe Best Mini PC for Local LLMs in 2026 (By Model Size and Budget)
From a $250 agent host to a 128GB box that runs 70B models, here's the right mini PC for local AI at every tier — matched to the model you actually want to run.
ReadFrequently Asked Questions
Why choose Apple silicon for local AI?
Bandwidth. The Mac Studio M3 Ultra runs at 819 GB/s — roughly 3× any Strix Halo or GB10 box — so it produces more tokens per second on the same model. The trade-off is that Apple runs MLX and llama.cpp only, with no CUDA.
Which Mac should I buy for local LLMs?
The Mac Studio M3 Ultra is the bandwidth king at 819 GB/s (96GB in 2026). The Mac Studio M4 Max reaches up to 546 GB/s with up to 128GB. The Mac Mini M4 Pro (273 GB/s, up to 64GB) is the value sweet spot, and the base Mac Mini M4 (120 GB/s) is the cheapest entry point.
How much unified memory do I need?
The base Mac Mini M4 tops out at 24GB and 120 GB/s — good for 7–14B-class models. For 70B-class models you want the M4 Max or M3 Ultra, where both the memory ceiling and the bandwidth are high enough to run them usefully.
Does Apple silicon run CUDA models?
No. Apple silicon runs MLX and llama.cpp only. Many AI tools and frameworks assume an NVIDIA GPU, so check that your runtime supports Apple silicon before committing.