Mac mini M4 Pro vs RTX 4090 — An End-to-End Local LLM Comparison: The Architecture Battle Between UMA and Discrete VRAM

Bottom Line First

For small-to-mid 8B-class models, the RTX 4090 is 2-3x faster. For large 27B-70B-class models, the Mac mini runs them stably while the RTX 4090 hits memory errors. Being fast and being able to hold something are different problems. This article draws that boundary line in numbers.

1. The Fundamental Architectural Difference: UMA vs Discrete VRAM

The two camps differ starting from their philosophy about memory.

[Apple Silicon UMA structure]
[ Unified memory (shared by CPU+GPU+NPU) ] -- shared bandwidth -- [ CPU / GPU / NPU ]
* The whole model resides in memory; the entire system acts like VRAM

[NVIDIA discrete structure]
[ System RAM ] -- PCIe bottleneck -- [ VRAM ] -- [ Tensor cores ]
* Beyond VRAM it PCIe-offloads, and speed collapses

2. The Single Variable That Determines Token Speed: Memory Bandwidth

LLM inference reads the entire model weights from memory on every token. So the theoretical speed is simple.

Theoretical TPS ≈ memory bandwidth / model size

The bandwidth ratio becomes the speed ratio directly. For an 8B Q4 (~5GB) model, the 4090 can deliver more than 3x the M4 Pro. Conversely, no matter how fast it is, it is useless if it does not fit in VRAM.

3. Measured Numbers, Small-to-Mid Models (8B-14B)

In the weight class where the model fits in both memories, bandwidth is the whole definition.

In short, the 4090 wins big in the small-to-mid range. For uses where perceived speed matters, such as real-time chat and coding autocomplete, the same model runs 2-3x faster. The base Mac mini M4 (16GB) has its sweet spot at the 7B class, and to look at the 13B class you need to go to 24GB or more.

4. Measured Numbers, Large Models (27B-70B)

Once a model passes 20GB, the arena flips.

From this weight class up, it is the Mac mini's solo stage. On popular 8-12GB GPUs most of the weights spill into system RAM and the thing grinds to a halt. The Mac mini is slow because its bandwidth is low, but it computes all the way to the end without stalling. It is a matter of whether you can hold it at all, and speed comes second.

5. Software Stack Differences

There is a real example. The Spark-X2.5-4B model (advertised as 1M context) could not even run locally because llama.cpp and Ollama did not recognize its architecture (based on measurement in the operator's environment). If you are aiming at a large-context model, check runtime support first. The 1M in the spec sheet is not the 1M that opens on your machine.

6. The Cost of Securing VRAM, in Money

For the same 2,000,000 KRW, the Mac side uses 48GB wholesale, while the NVIDIA side is the price of one 24GB card. The direction of value-for-money is the exact opposite.

7. Power, Heat, and Noise

If the goal is a silent always-on server on your desk, the Mac mini has the edge. It pairs well with running agents 24 hours without worrying about the power bill.

8. Final Selection Guide

Cases where you should go with an NVIDIA desktop workstation:

• When running 100+ t/s real-time chat and large batch pipelines mainly on light 8B-14B-class models
• When you control local fine-tuning and training directly in Python (training effectively forces CUDA)
• When you also want gaming and video work on the same machine

Cases where you should go with a Mac mini (M4 Pro):

• When you want to always-on Qwen 27B-class and 70B quantized models without them blowing up
• When you want to quietly run long-context agents and a RAG server 24 hours a day
• When you want to avoid the stress of electricity, noise, and heat

In one line: if speed is the goal, the 4090; if holding is the goal, the Mac mini M4 Pro.