What Can You Run on a MacBook Air With 16GB?

MacBook Air on a desk running an AI chat window

A base MacBook Air with 16GB of unified memory is one of the most common machines people try local LLMs (large language models) on. It works well, once you know what actually fits.

Unified memory is shared between the CPU and GPU, so the number on the box is not the number you get. macOS takes a couple of gigabytes, your browser takes some more, and what is left for the model is usually 12 to 13GB in practice.

That is enough for a good setup. A 7B or 8B model at Q4 quantization (4-bit) needs roughly 5 to 6GB for weights, plus another couple of gigabytes for the KV cache, which is the working memory the model uses to track the conversation, at reasonable context lengths. Llama 3.1 8B, Qwen 2.5 7B, and Mistral 7B all run comfortably and generate text at a usable speed.

Things get tight at 14B. A Q4 14B model wants about 9GB for weights alone, and once your context grows past 8k tokens, the KV cache eats the rest. Short chats work; long documents push the machine into swapping, and swapping on an Air means slow.

The practical move: start with an 8B model, keep context under 8k, and check the math before downloading a 14B. The VRAM calculator at llmhardware.io does exactly that.

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