The challenge: Memory for LLMs
To run an LLM locally, the number one limiting factor is available memory. A model must be loaded entirely into RAM (or VRAM) to function. And this is where architectures differ radically.
Apple Silicon
- Unified memory: All RAM is accessible to GPU + CPU
- MacBook Pro M3: up to 128 GB
- Mac Studio M2 Ultra: up to 192 GB
- Memory bandwidth: 400-800 GB/s
- ARM architecture optimized for Neural Engine
NVIDIA RTX
- Dedicated VRAM: GPU memory separate from system RAM
- RTX 4090: 24 GB VRAM (max consumer)
- RTX 6000 Ada: 48 GB VRAM (pro)
- VRAM bandwidth: 1000+ GB/s
- CUDA optimized, mature ecosystem
Understanding Apple unified memory
On Apple Silicon (M1, M2, M3, M4), memory is unified: the CPU and GPU share the same pool of RAM. Concretely:
- A 36GB MacBook Pro M3 can load a 30B Q5 (~26GB) model comfortably
- On PC, an RTX 4090 24GB would be limited to 13-14B models maximum, even with 64GB of system RAM
- No data copying between RAM and VRAM — everything is instantly accessible
💡 Concrete example: To run Llama 3.3 70B Q4 (~39GB), you need either a Mac Studio with 64GB+ of unified RAM, or a PC configuration with 48GB+ of VRAM (RTX 6000 Ada at €8000+). The Mac becomes economically more accessible for large models.
Benchmarks: M3 Max vs RTX 4090
Tests conducted with LM Studio, Qwen 3 8B Q5_K_M model, generating 512 tokens:
MacBook Pro M3 Max
36GB unified RAM
Tokens/second
PC RTX 4090
24GB VRAM + 64GB RAM
Tokens/second
Mac Studio M2 Ultra
192GB unified RAM
Tokens/second (70B)
Analysis of results
- Pure speed: NVIDIA RTX 4090 wins by 20-40% thanks to CUDA and higher VRAM bandwidth
- Accessible models: Mac M3 36GB can run models 2x larger than RTX 4090
- Mac Studio M2 Ultra: Only "accessible" hardware capable of running 70B+ models
- Consumption: Mac consumes 3-5x less energy (20-30W vs 150-450W)
Complete comparison table
| Criteria | MacBook Pro M3 Max | PC RTX 4090 | Winner |
|---|---|---|---|
| Memory for LLM | 36-128 GB (unified) | 24 GB VRAM max | Mac (capacity) |
| Generation speed | 35-40 tok/s | 50-60 tok/s | NVIDIA |
| Max accessible model | 70B Q4 (128GB Mac) | 30B Q4 (24GB VRAM) | Mac (capacity) |
| Configuration price | €4000-7000 | €2500-3500 | NVIDIA |
| Power consumption | 20-40W | 150-450W | Mac |
| Portability | Native laptop | Desktop (heavy) | Mac |
| Ecosystem | Limited (Metal) | Rich (CUDA) | NVIDIA |
| Noise / Heat | Silent | Noisy under load | Mac |
Which hardware to choose?
💻 For small models (7-8B)
Tight budget, lightweight models: both platforms excel. A MacBook Air M3 16GB or a PC with RTX 3060 12GB will do perfectly.
- MacBook Air M3 16GB: ~€1400, silent, portable
- PC + RTX 3060 12GB: ~€1000, faster, desktop
🖥️ For medium models (13-30B)
This is where Apple unified memory becomes decisive.
- MacBook Pro M3 36GB: ~€3200, can comfortably run 30B models
- PC + RTX 4090 24GB: ~€3500, limited to 13-14B on GPU (rest on CPU possible but slow)
🚀 For large models (70B+)
Apple Silicon is practically alone in this "accessible" segment.
- Mac Studio M2 Ultra 128GB: ~€7000, can run Llama 3.3 70B Q4
- PC Alternative: RTX 6000 Ada 48GB (~€8000) + 128GB RAM, but complex to configure
🏆 Verdict by usage:
- Mobile/developer usage: MacBook Pro M3 — silence, battery, memory capacity
- Pure performance / Gaming: PC NVIDIA — speed, CUDA ecosystem
- Large 70B+ models: Mac Studio — only "reasonable" option
- Tight budget: PC RTX 3060/4060 — best performance/price ratio
Conclusion
The choice between Apple Silicon and NVIDIA for LLMs depends on your priority: pure speed (NVIDIA) vs memory capacity (Apple).
In 2026, Apple Silicon emerges as the ideal platform for advanced local AI thanks to its generous unified memory. Being able to run a 70B model on a "consumer" desktop computer was impossible before the Mac Studio.
That said, for the vast majority of users with 7-14B models, both platforms offer an excellent experience. LocalClaw will help you optimize your settings regardless of your configuration.