RTX 4090 vs RTX 5090 vs Mac Studio for Local LLMs
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If you want to run language models on your own machine, the choice usually comes down to three options: an RTX 4090, an RTX 5090, or a high-memory Mac Studio. They solve the problem in different ways — raw GPU speed versus large unified memory. Here is how they compare on the things that decide which models you can actually run.
VRAM and the model size you can run
The biggest practical difference is memory. The RTX 4090 has 24GB, which handles models up to about 32 billion parameters at 4-bit. The RTX 5090 has 32GB, giving more headroom for long context and tight 70B runs. A Mac Studio with 64–128GB of unified memory runs 70B models comfortably, because the chip can use almost all that memory as if it were VRAM.
Speed
For models that fit in VRAM, the NVIDIA cards are faster. The RTX 5090 leads on memory bandwidth, so it generates tokens quicker than the 4090, which in turn beats the Mac on pure throughput. The Mac wins when a model is too big to fit on a single card — slower per token, but able to run something the GPUs simply cannot hold.
Value for UAE buyers
The RTX 4090 is the most capability per dirham if 24GB is enough. The RTX 5090 is the pick for speed and a little more room. The Mac Studio is the value choice for running genuinely large models on one quiet, low-power machine instead of a multi-GPU build.
- Best budget capability: RTX 4090 24GB
- Best speed and headroom: RTX 5090 32GB
- Best for large models on one box: Mac Studio M-Max / M-Ultra
Switching between them
Many people start on a GPU and later move to a Mac Studio for bigger models, or step from a 4090 to a 5090. You do not have to carry two machines. Trade the old one toward the new and pay only the difference — SellYourMac.ae handles GPU and Mac trade-ins both ways across the UAE.
Frequently asked
Is the RTX 5090 worth it over the 4090 for AI?
If you need the extra speed or want to run 70B models at low quant, yes. For models up to 32B, the cheaper RTX 4090 24GB is enough.
Can a Mac Studio replace a GPU for local AI?
For large models, yes — its unified memory runs models a single 24–32GB card cannot hold. For smaller models that fit in VRAM, a GPU is faster.
