The best Mac for local LLMs.

1Why memory and bandwidth decide everything

A language model writes one token at a time, and for each one it reads every active weight from memory. So the speed limit for a single conversation is roughly memory bandwidth divided by the size of the active weights. In llama.cpp's public benchmarks, real generation reaches 43% to 80% of that limit on Apple chips (llama.cpp).

Reading your prompt is different: that part is limited by compute, which is why M5-generation chips, with neural accelerators in the GPU, process prompts several times faster than earlier ones (Apple).

2Every chip, compared

Older chips still work well when they have the memory. An M1 Max or M2 Max with 64 GB reads at 400 GB/s, more than any base chip today, and often costs far less second-hand.

3Which Mac to buy

Just trying it: MacBook Air or Mac mini with 16 to 32 GB

Runs 7B to 14B models comfortably and 30B-class models at 32 GB. Fine for chat, writing and light coding help.

Best value: Pro chip with 64 GB

A Mac mini or MacBook Pro with M4 Pro or M5 Pro holds 70B-class models and runs mid-size models at a good pace. The sweet spot for most developers.

Serious local AI: Max chip with 128 GB

Runs gpt-oss-120b (about 65 GB at 4-bit) on a laptop; from Apple's bandwidth figure, the theoretical ceiling for one stream on M5 Max is about 225 tokens a second. The best portable choice.

The biggest models: Mac Studio with Ultra

Up to 512 GB at 1.2 TB/s with M5 Ultra holds models with hundreds of billions of parameters on one desk. For even bigger models, Macs can be joined into a cluster over Thunderbolt 5: see Mac AI clusters.

4Make the Mac pay its way

A Mac bought for AI spends most of its time waiting. MLX Node is a free app that lends that spare capacity to MLX Network, running AI jobs on the same MLX framework and earning MLX Tokens for verified work. A dedicated seat matched to the chip multiplies what it earns: a Pro seat runs at full rate on a Pro or Max chip, an Ultra seat on an Ultra chip.

Compare your Mac on MLX Bench first: it shows measured tokens per second for open models on every chip.

5Questions

Is 16 GB enough for a local LLM?

Yes for models up to about 14B parameters at 4-bit. For larger models, or long contexts, 32 GB or more is much more comfortable.

Mac Studio or MacBook Pro for LLMs?

Both come with Max chips and up to 128 GB. The MacBook Pro is portable; the Mac Studio stays cooler under long loads and is the only Mac with Ultra chips and up to 512 GB.

Is a Mac better than an Nvidia GPU for local AI?

For the largest models at home, often yes: a Mac's unified memory can hold far more than a consumer graphics card's memory. A high-end Nvidia card is faster on models that fit in its memory.

Does the M5 make LLMs faster?

Yes, mainly for reading prompts: Apple measured the first token arriving 3.3 to 4.1 times sooner on M5 than M4 with MLX. Generation speed grows with memory bandwidth, which rose 28% from M4 to M5.