The question was basic but timely: what does a MacBook actually do differently from a box with a dedicated GPU when you run local LLMs, and how do you estimate what a Mac can handle? The clearest answer was that Apple Silicon behaves like a relatively slow GPU attached to a lot of memory. Because the CPU and GPU share one memory pool, a Mac with 64GB or 128GB can load model sizes that would not fit on a single consumer Nvidia card. The tradeoff is speed. Dedicated GPUs have much higher compute and memory bandwidth, so they answer faster, especially on prompt ingestion and time to first token.
That memory point turned into the practical rule of thumb the original poster was looking for. On Macs, the GPU does not have a fixed
VRAM partition. It draws from system RAM, with commenters putting the usable share for models at roughly 70 to 80 percent after macOS and other apps take their cut. In plain terms, a 64GB Mac often means something like 45GB to 50GB available for model weights and runtime overhead. That makes Macs attractive for running larger quantized models locally, but not for making them feel snappy.
People with hands-on numbers mostly backed that split. Several reported decent local performance on recent M-series machines with Qwen and Gemma class models, but Nvidia setups were still multiple times faster on both
prefill and
decode. The most repeated complaint about Macs was not raw
tokens per second. It was latency before the first answer appears, which gets worse as context grows. For coding and interactive chat, that delay is what makes a Mac feel slow even when total throughput looks respectable on paper.
The more useful consensus was that this is not really a Mac versus PC identity fight. It is a workload choice. If you want a laptop anyway, value silence, power efficiency, privacy, and zero per-token cloud cost, a Mac with lots of RAM is a legitimate local
LLM machine and a particularly good one for learning. If your goal is specifically LLM performance per dollar, or you also want fine-tuning and the broader machine learning toolchain,
CUDA still wins hard. A few commenters recommended a hybrid setup as the current sweet spot: use a Mac as the quiet front end and remote into a Linux workstation or cloud GPUs when you need real speed.