Local LLMs on Apple Silicon: A Practical Guide to Ollama and MLX
The case for running large language models locally has never been stronger. Privacy guarantees (zero data leaving your machine), predictable latency (no network jitter), zero per-token API costs, and offline capability are compelling advantages for any development workflow that touches LLMs.
Apple Silicon’s unified memory architecture makes it uniquely capable for local inference at the consumer and professional level. Unlike discrete GPU setups where VRAM is a hard ceiling, Apple’s unified memory pool is shared between CPU and GPU. A MacBook Pro with 64 GB of unified memory can load models that would require a $20,000+ datacenter GPU to match.
