Apple Silicon’s unified memory architecture turned Macs into genuinely capable local LLM inference machines — a somewhat unexpected strength for hardware not originally marketed around AI workloads specifically.
Why Unified Memory Matters Here
On a typical PC, the GPU has its own separate, dedicated VRAM, distinct from system RAM — a large model that doesn’t fit in VRAM simply can’t run on the GPU at full speed. Apple Silicon’s unified memory architecture lets the GPU access the same pool of RAM as the CPU, meaning a Mac with 64GB or 128GB of unified memory can load and run genuinely large models that would need an equivalently enormous (and vastly more expensive) discrete GPU on a traditional PC.
# Ollama and llama.cpp both support Apple's Metal API for GPU acceleration brew install ollama ollama pull llama3.1:70b # a model that would need a very expensive discrete GPU setup elsewhere ollama run llama3.1:70b
The Genuine Trade-off
Apple Silicon’s memory bandwidth, while genuinely good, still trails dedicated high-end discrete GPUs in raw throughput — meaning while a Mac can genuinely load and run larger models than a comparably priced discrete-GPU PC, token generation speed on very large models can be noticeably slower than on a dedicated high-VRAM Nvidia setup, a real speed-versus-capacity trade-off worth weighing based on your actual priority.