M4 Mac Mini with 64GB versus a custom PC with an RTX 4090 for local AI work. Which would you buy today? - the honest answer

[Cole_25]

The comparison keeps coming up and it is genuinely non-obvious. The Mac Mini at 64GB unified memory costs significantly less than a 4090 build, handles 70B parameter models at 4-bit quantisation at usable speeds, and uses a fraction of the power. The 4090 has CUDA, 24GB VRAM, and faster inference on models that fit in VRAM.

Which do you buy if you are starting from scratch today and your primary use case is running local AI models?

[SortedBuilder]

Mac Mini for most people. The unified memory architecture means you are not fighting the VRAM ceiling on medium models. The power draw is a real monthly consideration

[BiscuitTin46]

4090 if your work requires CUDA. The PyTorch and ML framework ecosystem is still better optimised for CUDA than Metal. That gap is closing but it exists

[Oscar_86]

Used 3090 from the second-hand market is the third option nobody mentions. 24GB VRAM, same as the 4090, fraction of the price. The bandwidth gap is real but acceptable

[HeartbreakKidCurtis18]

Mac Mini if you are on macOS anyway. Switching operating systems to get better AI inference is a worse trade than the inference quality difference

[Fan]

The power cost is underweighted in most comparisons. A 4090 at load adds meaningful cost per month. Over a year that approaches the price difference between the two options

[TeaAndCode72]

NVLink two 4090 setup for 48GB VRAM is the serious local AI workstation and costs roughly three times the Mac Mini. Justify that against your use case before buying

[Mike80]

The answer changes completely if you need to run models larger than 70B. Then unified memory advantage matters less and you need actual VRAM

[BradBytheway]

Apple Silicon trajectory matters. Each generation improves inference performance. The Mac Mini M4 you buy today will be relatively worse in eighteen months. The 4090 will not change