The old shorthand was that Apple sells you capacity and NVIDIA sells you bandwidth. That stopped being true at the top of Apple’s line. The M5 Ultra carries up to 512GB of unified memory at 1.2TB/s — more memory bandwidth than an RTX 4090 and sixteen times the capacity of an RTX 5090.
It is still true everywhere else. Below the Ultra, Apple’s bandwidth drops off a cliff: an M6 Mac mini moves 153GB/s against the RTX 5090’s 1,792GB/s. Which platform wins depends almost entirely on which rung you are buying, and that is what this comparison is about. For whether the cheapest Apple machine is enough on its own, see our can the Mac Mini run LLMs guide.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB GDDR6X and full CUDA support. The fastest local LLM inference and the only consumer card worth using for fine-tuning workflows.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
The fundamental difference: unified memory vs dedicated VRAM
This is the core tradeoff:
Apple Silicon uses unified memory — the same pool serves both CPU and GPU, so a Mac Studio configured with 512GB can put essentially all of it behind model weights. A 70B model at Q4_K_M is 43GB, which a 64GB machine already holds comfortably; the reason to go higher is 200B+ models and long context, not 70B. (For the Mac-specific buyer’s guide covering MLX + Ollama integration and which M-tier fits which model size, see our best GPU for MLX guide.)
NVIDIA uses dedicated VRAM on the GPU die, physically separate from system RAM. An RTX 4090 has 24GB, period. Loading anything larger requires multi-GPU setups or offloading — which tanks inference speed.
| Attribute | Apple Silicon | NVIDIA GPU |
|---|---|---|
| Max single-device memory | 512GB (M5 Ultra) | 32GB (RTX 5090) |
| Memory bandwidth | 153 GB/s (M6) to 1.2 TB/s (M5 Ultra) | 288 GB/s (4060 Ti) to 1,792 GB/s (5090) |
| Architecture | Unified (CPU+GPU share) | Dedicated VRAM |
| Best for | Large model inference | Fast inference, training |
| OS support | macOS only | Linux, Windows, macOS |
| CUDA support | No | Yes |
Memory bandwidth: the number that sets token speed
Token generation reads the whole model out of memory once per token, so on both platforms the speed ceiling is bandwidth, not compute. That makes bandwidth the one figure worth comparing directly — and unlike tok/s, both vendors publish it.
| Device | Memory | Memory bandwidth |
|---|---|---|
| RTX 5090 | 32GB dedicated | 1,792 GB/s |
| M5 Ultra (Mac Studio) | up to 512GB unified | 1,200 GB/s |
| RTX 4090 | 24GB dedicated | 1,008 GB/s |
| M5 Max, 40-core GPU | up to 128GB unified | 614 GB/s |
| M5 Max, 32-core GPU | 36-128GB unified | 460 GB/s |
| M5 Pro (Mac mini) | up to 64GB unified | 307 GB/s |
| RTX 4060 Ti 16GB | 16GB dedicated | 288 GB/s |
| M6 (Mac mini) | 16-32GB unified | 153 GB/s |
Apple figures are from the Mac Studio and Mac mini spec pages; NVIDIA’s are manufacturer specifications. Note that the M5 Max and M5 Ultra Mac Studio configurations are pre-order at the time of writing, shipping 22 September.
Bandwidth is a ceiling, not a rate. Our modelled tok/s figures put an RTX 5090 near 95 tok/s and an RTX 4090 near 65 on a 7B model at Q4 — roughly a quarter to a third of what raw bandwidth would allow, because a runtime never achieves peak. Metal and MLX sit below CUDA on that efficiency curve, so an M5 Max at 614 GB/s will land below the 61% of an RTX 4090 that the bandwidth ratio suggests, not at it. How we derive those numbers is on our methodology page; treat them as a ranking, not a promise.
Check RTX 4090 Price→Buy on Shopee SG→Large model inference: where Mac genuinely wins
Load Llama 3 70B at Q4_K_M (43GB). Your options:
- M5 Max Mac, 64GB: holds it with room for context, on one device, near silently.
- M5 Max Mac, 128GB: holds it with room for a second model resident at the same time.
- M5 Ultra Mac Studio: holds it several times over — this tier is aimed at 200B+ models, not 70B.
- RTX 4090 alone: cannot fit it. 43GB of weights against a 24GB card.
- RTX 5090 alone: 32GB card, so q3_K_M at 34GB is already over. It tops out at q3_K_S (31GB), with degraded quality and no context headroom.
- 2x RTX 4090: fits at Q4_K_M, and costs ~$4,400 in GPUs plus a motherboard that can take both.
This is the tier where the Mac argument is strongest, and it is a capacity argument rather than a speed one. Two 4090s will generate tokens faster than any Mac; what they will not do is fit in a quiet box on a desk, or scale past 48GB without another card and another slot.
Software ecosystem: NVIDIA’s real advantage
CUDA is the bedrock of the LLM software stack:
| Tool | NVIDIA (CUDA) | Apple (Metal/MPS) |
|---|---|---|
| Ollama | Native, fast | Supported |
| llama.cpp | cuBLAS backend | Metal backend |
| vLLM | Full support | Not supported |
| ExLlamaV2 | Full support | Not supported |
| Fine-tuning (LoRA) | Full support | Limited/slow |
| PyTorch training | First-class | MPS backend, gaps |
| GPTQ / AWQ quants | Full support | Limited |
Mac runs Ollama and llama.cpp well. Anything beyond basic inference — production serving with vLLM, fine-tuning with LoRA, or advanced quantization formats — requires NVIDIA.
Need CUDA for training? Try RunPod→Which use case fits which platform?
Mac wins for:
- Running 30B-70B models on a single device
- Quiet, integrated, always-on personal assistant setups
- Privacy-first inference with no separate GPU box
- Users who already work in macOS and want zero friction
NVIDIA wins for:
- Fastest token throughput on 7B-14B models
- Fine-tuning and LoRA training workflows
- Production LLM serving with vLLM
- Advanced quantization formats (GPTQ, AWQ, EXL2)
- Linux-first or Windows-first environments
Which platform should YOU choose?
- You want to run 7B-14B models fast and cheap? NVIDIA RTX 4060 Ti 16GB (~$425). Its 288 GB/s beats an M6 Mac mini’s 153 GB/s at a fraction of the price, as a card added to a machine you already own.
- You want to run 34B-70B models without multi-GPU complexity? M5 Max Mac, 64GB or 128GB. The unified memory advantage is decisive at this model tier.
- You do fine-tuning or LoRA training? NVIDIA, full stop. Mac’s MPS backend for training is functional but significantly slower and missing key optimizations.
- You want an all-in-one quiet personal AI machine? Mac. The integrated experience with no extra boxes or power draw is unmatched.
- You want maximum inference speed per dollar? NVIDIA. A ~$425 RTX 4060 Ti outperforms every Mac below the M5 Max on 7B-14B inference.
Common mistakes to avoid
- Comparing GPU price to Mac system price. An RTX 4090 at $2,200 still needs a PC around it. A Mac is a complete computer. Compare total system cost, and note that Apple prices unified memory steeply — the configurator, not the headline price, decides what a large-model Mac costs.
- Assuming every Apple chip has the bandwidth of the flagship. The gap inside Apple’s own line is larger than the gap between Apple and NVIDIA: 153 GB/s in an M6 Mac mini against 1.2 TB/s in an M5 Ultra is nearly 8x. “A Mac” is not a specification.
- Buying a Mac expecting CUDA compatibility. Rosetta does not translate CUDA. vLLM, ExLlamaV2, and many training frameworks simply will not run on macOS. Check your toolchain before buying.
- Ignoring Ollama on Mac. Ollama’s Metal backend on Apple Silicon is polished and reliable. For casual local inference, the Mac experience is genuinely good.
Final verdict
| Goal | Platform | Cost |
|---|---|---|
| Fast 7B-14B inference | NVIDIA RTX 5060 Ti 16GB | ~$630 (GPU only) |
| Cheapest useful 16GB card | NVIDIA RTX 4060 Ti 16GB | ~$425 (GPU only) |
| Best all-round inference | NVIDIA RTX 4090 | ~$2,200 (GPU only) |
| 34B-70B on one device | M5 Max Mac, 64GB+ | Apple configurator |
| 200B+ models on one device | M5 Ultra Mac Studio | Apple configurator |
| Fine-tuning / training | NVIDIA RTX 4090 | ~$2,200 (GPU only) |
GPU prices are street prices and move; the Mac rows are left to Apple’s configurator because unified memory is the option that drives a Mac’s price, and quoting a base price next to a 128GB requirement would understate it by thousands.
NVIDIA GeForce RTX 5090
32GB GDDR732GB GDDR7 — the only single NVIDIA consumer card that approaches Mac's large-model advantage, while keeping CUDA's speed and ecosystem benefits.
Affiliate links — we may earn a commission at no extra cost to you. Amazon ships globally; Shopee SG covers Singapore & ASEAN.
For Ollama-specific GPU advice on NVIDIA, see best GPU for Ollama. Need a VRAM reference for your target model size? See how much VRAM for local LLM. Comparing NVIDIA to AMD instead? See NVIDIA vs AMD for LLM. If you prefer LM Studio’s graphical interface over Ollama, see our best GPU for LM Studio guide for hardware picks tuned to that tool.
Pick Mac if unified memory solves a size problem you cannot solve with affordable NVIDIA hardware. Pick NVIDIA if speed and the CUDA ecosystem matter more than model size headroom.
Frequently asked questions
Is a Mac or an NVIDIA GPU better for running LLMs locally?
It depends which limit you hit first. NVIDIA is faster per dollar on anything that fits a graphics card, and it owns the CUDA tooling. Apple wins when the model stops fitting: unified memory lets one machine hold weights no consumer card can, quietly and on modest power. Below Apple’s top tiers, though, a mid-range NVIDIA card has more bandwidth than a Mac mini at a fraction of the price.
Can an RTX 5090 run a 70B model on its own?
Only at a heavily reduced quantization. Llama 3 70B is 43GB at Q4_K_M against 32GB of VRAM, and even q3_K_M is 34GB — still over. The largest build that fits is q3_K_S at 31GB, which leaves nothing for context and costs noticeable quality. A 4090 cannot hold any 70B build at all.
How much memory does a Mac need for a 70B model?
64GB of unified memory holds a 70B at Q4_K_M with room for context, on a single machine. 128GB lets you keep a second model resident alongside it. The 512GB tier exists for 200B-class models and is overkill for 70B.
Why is memory bandwidth the number that matters?
Because generating each token reads the whole model out of memory once, so on both platforms the ceiling is bandwidth rather than compute. That is also why Apple’s line splits so sharply: the gap inside it, from a Mac mini to an Ultra, is larger than the gap between Apple and NVIDIA.
Is it cheaper to buy a Mac or build an NVIDIA machine?
Compare whole systems, not a GPU against a Mac. A graphics card still needs a computer around it, and a Mac is one already. For small models NVIDIA wins on price for the speed you get; at the sizes where you would need several cards and a motherboard and power supply to match them, a single large-memory Mac starts to look reasonable.