Apple Silicon
Apple's ARM-based system-on-chip family (M-series) that integrates CPU, GPU, and unified memory on a single die, used in Macs for local LLM inference.
Apple Silicon refers to Apple's in-house ARM-based system-on-chip (SoC) family — the M1, M2, M3, and M4 series — that powers modern Macs. For local AI builders, the relevant trait is the SoC's unified memory architecture, which lets the GPU address the same pool as the CPU instead of being capped by a separate VRAM buffer.
SoC Architecture and Memory Pool
On Apple Silicon, CPU cores, GPU cores, and the Neural Engine all share one physical memory pool over a wide on-package bus. High-end variants like the M3 Max, M4 Max, and Ultra ship with up to 128GB of unified memory, which the GPU can use in full for model weights and KV-cache. That removes the discrete-GPU ceiling that forces dedicated rigs into VRAM offloading once a model exceeds card capacity.
Software Stack and Tradeoffs
Inference on Apple Silicon runs through MLX (Apple's array framework) or llama.cpp with Metal backend, typically loading GGUF quantized weights. The win is capacity: a 128GB Mac Studio can hold a 70B model at 4-bit without splitting layers across devices. The cost is raw compute and memory bandwidth — even Ultra-tier chips trail a discrete NVIDIA card on tokens-per-second, and the CUDA-only ecosystem (xformers, vLLM, many fine-tuning stacks) is unavailable. There is no CUDA, no tensor cores, no ROCm — just Metal and MLX.
Why It Matters for Local AI
Apple Silicon reshapes the cost-per-GB-of-model math. Instead of stacking two or three discrete GPUs to reach 48–72GB of VRAM, an M3/M4 Max or Ultra puts 64–128GB of model-addressable memory in one quiet box. For builders prioritizing model size over decode speed — running 70B-class models for chat, RAG, or coding rather than batch serving — it's the simplest path to "no offload, no split" inference.