Mac mini vs NVIDIA GPU for Local LLMs

This comparison is architectural. A Mac mini is a complete computer with fixed unified memory. A used RTX 3090 is a replaceable accelerator that requires a host. Their listing prices do not describe the same purchase.

For broad local-AI tooling and models that fit in 24GB, the RTX 3090 remains the faster, more flexible platform. For larger quantized models, a quiet desktop appliance, and workflows that MLX supports well, a 48GB or 64GB M5 Pro Mac mini can be the better machine.

Memory fit comes first

Apple sells the current Mac mini with two chips. The M6 model offers 16GB, 24GB or 32GB of unified memory at up to 170 GB/s. The M5 Pro model offers 24GB, 48GB or 64GB at 307 GB/s. For local LLMs the M5 Pro is the relevant choice, because only it goes past 32GB.

On NVIDIA, model weights, context, cache, and runtime allocations must fit in VRAM or spill into system RAM. On Apple Silicon, CPU and GPU share unified memory, but macOS and other applications also consume that pool. A 64GB Mac can hold a 32B model at Q8_0, which no 24GB card can, but a 24GB Mac has less usable model memory than a 24GB RTX 3090.

Choose the Mac’s memory at purchase because it cannot be upgraded. Choose an NVIDIA host with enough system RAM and PSU and cooling capacity to support the current card and a likely successor.

Bandwidth decides generation speed

Generating each token means reading the model’s weights from memory, so memory bandwidth sets the speed ceiling. The RTX 3090’s 936 GB/s is about three times the M5 Pro’s 307 GB/s. For a model that fits on both, expect the 3090 to generate faster. The Mac’s advantage is capacity: it can run models the 3090 cannot hold at all. The VRAM planning guide shows how to size them.

Software decides the useful hardware

CUDA has the broader ecosystem across inference servers, training tools, image generation, and third-party extensions. MLX is Apple’s array framework for machine learning on Apple silicon, and llama.cpp and Ollama run on Macs through Metal. A CUDA-only project is not made portable merely by having enough unified memory.

List the actual applications before choosing. “Local AI” is too broad a workload definition.

Compare complete-system cost

The Mac price includes CPU, memory, storage, enclosure, networking, and operating system. Apple lists the M5 Pro Mac mini from $1,699 with 24GB, and the 48GB and 64GB configurations cost more. An RTX 3090 listing excludes the host, power supply, case, storage, and the time required to validate a used card.

Operating cost also depends on duty cycle. Apple rates the whole Mac mini at 155W maximum continuous power, while the RTX 3090 alone has a 350W board rating. A personal assistant that generates for minutes per day is not a 24/7 full-load GPU, so measure idle and active wall power and use your local electricity rate.

Bottom line

Buy the used RTX 3090 when CUDA breadth, speed on models that fit in 24GB, and replaceable hardware are the priorities. Buy a 48GB or 64GB M5 Pro Mac mini, which Apple sells build-to-order, when larger models, quiet complete-system operation, and MLX support are more valuable. The M5 Pro Mac mini on Amazon is the 24GB model, which holds less than the 3090 can. Buy neither until the exact model and application stack are known.