Choosing a Mac for running local LLMs comes down to one number above everything else: unified memory. Unlike a Windows PC where the GPU has its own VRAM and the RAM is separate, Apple Silicon shares one memory pool between CPU and GPU. This means every gigabyte of RAM you buy is available to your language model — no VRAM ceiling, no model splitting. The question is how much you need for the models you want to run, and whether the speed and efficiency tradeoffs of different chips matter for your use case.
Why Apple Silicon Is Good for Local LLMs
Three properties make Apple Silicon well-suited for local LLM inference. Unified memory means a 64GB Mac can load a 64GB model — a GPU PC needs a $3,000+ card to match that. Memory bandwidth is extremely high relative to the chip’s power consumption — the M4 Max delivers around 546 GB/s, which directly translates to tokens per second because LLM decode is bandwidth-bound rather than compute-bound. Power efficiency means running models overnight or continuously costs a fraction of what an equivalent GPU PC consumes — a Mac Mini M4 draws around 20–25W under LLM load versus 350–450W for a desktop GPU machine.
The Memory Tiers That Actually Matter
For local LLM use, unified memory breaks into four practical tiers based on which models fit:
- 16GB — runs models up to about 13B at Q4 quantisation (approximately 8–9GB). Covers Qwen2.5-7B, Llama 3.2 8B, Phi-4-mini comfortably. Cannot run 14B+ models. Sufficient for casual use and coding assistance but limiting for power users.
- 24GB — runs 13B–14B models at Q4 (approximately 9–10GB) with comfortable headroom for context and KV cache. Qwen2.5-14B, Phi-4 (14B), and DeepSeek-R1-14B all fit well. The sweet spot for most developers.
- 48GB — runs 32B–34B models at Q4 (approximately 20–22GB) and 27B models with room for long contexts. Enters territory where model quality is noticeably better than 14B for complex reasoning tasks.
- 64GB+ — runs 70B models at Q4 (approximately 42GB). 70B models on a Mac are a genuine differentiator — no consumer GPU card matches this without expensive multi-GPU setups. Llama 3.3 70B, Qwen2.5-72B at reduced quantisation.
Figure 1 — Mac buying guide for local LLMs: which tier for which use case
MacBook Air M4: The Portable Option
The MacBook Air M4 starts at 16GB unified memory and is the most affordable entry point. For casual local AI use — running a 7B assistant model, coding assistance with Qwen2.5-Coder-7B, using Continue.dev or LM Studio — 16GB is sufficient. The Air’s fanless design means it throttles under sustained inference load, which matters for batch processing or long-running tasks. For interactive use where you prompt the model, wait for a response, and prompt again, the thermal throttling rarely kicks in. The 16GB base model is the minimum recommended; if budget allows, upgrading to 24GB (available on the M4 Air with the Plus tier chip) opens up the 14B model range without throttling concerns.
Mac Mini M4 Pro: The Recommended Starting Point
The Mac Mini M4 Pro with 24GB is the recommended starting configuration for most developers and power users coming from the Bing query “which Mac to buy for local AI.” It delivers the M4 Pro chip’s higher memory bandwidth over the base M4, 24GB for comfortable 14B model inference, active cooling that eliminates thermal throttling under sustained load, and desktop form factor at a competitive price. The step to 48GB on the same M4 Pro chip is worth taking if budget allows — it opens the 32B tier which is a meaningful quality jump over 14B for complex reasoning tasks. The Mac Mini is also easily the cheapest way to build a local LLM server on Apple Silicon that runs 24/7 quietly and efficiently.
Mac Studio M4 Max: When 70B Matters
The Mac Studio with M4 Max and 64GB unified memory is the entry point for running 70B models at practical speeds. On a 64GB Mac Studio, Llama 3.3 70B at Q4 (approximately 42GB) runs at around 15–20 tokens per second — fast enough for interactive use. This is the tier where Apple Silicon genuinely has no affordable Windows PC equivalent: a GPU PC capable of fitting a 70B model in VRAM requires multiple high-end cards or a specialised AI workstation costing several times more. If your work involves complex reasoning, document analysis over very long contexts, or you want near-frontier quality from a local model, the Mac Studio 64GB is the inflection point where the premium becomes clearly justified.
What About the MacBook Pro?
The MacBook Pro M4 Pro and M4 Max offer the same chips as the Mac Mini and Mac Studio respectively, with the addition of portability and a display. If you need a portable machine that also handles serious local LLM inference, the MacBook Pro is the right choice. If the machine will primarily live on a desk, the Mac Mini or Mac Studio at the same chip tier offers better value. The MacBook Pro’s active cooling handles sustained inference without throttling, unlike the MacBook Air — an important consideration if you run long batch jobs or use the model as a continuous background service.
The 8GB Question
An 8GB Mac — the base MacBook Air M4 — can technically run local LLMs, but the experience is limited. Models up to 7B in Q4 (around 4–5GB) fit, but with only 3–4GB left for the operating system, you’ll experience memory pressure and slowdowns during inference. For occasional, casual use it works. For any serious development, coding assistance, or daily driver use, 8GB is too constrained. The 16GB configuration is the practical minimum for a satisfactory local LLM experience.
Memory Bandwidth: Why Chip Generation Matters
Within each memory tier, the chip generation determines inference speed. The M4 generation delivers approximately 20–40% higher memory bandwidth than equivalent M3 chips, which translates directly to faster token generation. M4 Max at 64GB delivers around 546 GB/s versus the M3 Max’s approximately 400 GB/s — roughly 36% faster token generation for the same model. If you are buying new, buy M4. If you already have an M3 machine and are evaluating whether to upgrade purely for LLM speed, the improvement is meaningful but may not justify the cost unless inference speed is a daily bottleneck.
Frequently Asked Questions
- Can I upgrade RAM in a Mac after purchase?
- No. Unified memory is soldered to the Apple Silicon chip — it cannot be upgraded after purchase. Choose your RAM configuration carefully when ordering. When in doubt, buy more RAM than you think you need: LLM models and context windows will only grow.
- Is a Mac better than a Windows PC with an NVIDIA GPU for local LLMs?
- It depends on model size. For models up to 34B, an RTX 4090 (24GB VRAM) is faster than a comparable Mac. For 70B models, a 64GB Mac Studio has no affordable PC equivalent. For users who want to run 70B+ locally, Mac is the clear winner. For users who primarily run 7B–34B models, a GPU PC offers faster inference per dollar.
- What software should I use for local LLMs on Mac?
- Ollama is the recommended starting point — it handles model downloads, runs with Metal GPU acceleration automatically, and exposes an OpenAI-compatible API. LM Studio provides a graphical interface if you prefer not to use the command line. For maximum performance on Apple Silicon, the MLX framework via mlx-lm delivers the best tokens-per-second numbers.
- Does the Neural Engine help with LLM inference on Mac?
- Not significantly for current LLM workloads. The Neural Engine is optimised for specific Apple frameworks (Core ML) and certain quantised model formats. Most LLM inference through Ollama and llama.cpp uses the GPU (Metal) rather than the Neural Engine, which provides better performance for the autoregressive generation pattern LLMs use.
Last updated: October 2026
Mac vs Windows PC: The Short Answer
For 7B–34B models, a Windows PC with an RTX 4090 (24GB VRAM) is faster and cheaper than a Mac at the same price point. For 70B models, a 64GB Mac Studio has no affordable PC equivalent — a GPU PC that can fit 70B in VRAM requires a $4,000+ card or multi-GPU setup. The decision simplifies to: if you primarily run models under 34B and want maximum tokens per second per dollar, buy a GPU PC. If you want to run 70B locally, value silence and efficiency, work primarily on macOS, or want a single machine that handles both local AI and daily work without separate hardware, Mac is the better choice. For most developers who are not sure which direction to go, the Mac Mini M4 Pro 24GB at its current price point is the safest entry into serious local AI that does not require a separate Windows machine or GPU setup.
Storage Considerations
Model files are large — a 7B model in Q4 is around 4–5GB, a 14B model around 9GB, and a 70B model around 42GB. If you plan to maintain a library of several models across different sizes, storage adds up quickly. The Mac Mini and Mac Studio base storage configurations are modest; upgrading storage at purchase is significantly cheaper than external SSDs or cloud storage for model files. For a 24GB Mac used for local AI, 512GB of internal storage is a comfortable minimum; 1TB is recommended if you plan to keep several models downloaded simultaneously. Unlike RAM, storage does not affect inference speed — models load into unified memory before inference begins — but insufficient storage means constantly deleting and re-downloading models.