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: configurations up to 128 GB
- Current Mac Studio M3 Ultra: up to 512 GB
- Current Mac Studio bandwidth: up to 546 GB/s (M4 Max) or 819 GB/s (M3 Ultra)
- ARM architecture optimized for Neural Engine
NVIDIA RTX
- Dedicated VRAM: GPU memory separate from system RAM
- RTX 4090: 24 GB VRAM
- RTX 5090: 32 GB VRAM (current consumer flagship)
- 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 can keep roughly 20-24GB of model data and context on the GPU; larger models can spill into system RAM, but generation becomes slower
- 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.
Performance: compare like with like
We removed the old single-number token-per-second chart because it did not document runtime version, prompt processing, context length, temperature or repeatability. Those variables can change the result substantially.
Apple Silicon
Large unified-memory pool
Best when model size is the constraint
RTX 4090
24GB dedicated VRAM
Strong mature acceleration when the model fits
RTX 5090
32GB dedicated VRAM
More consumer VRAM for larger Q4 models
How to benchmark your own machine
- Keep the model and quantization identical.
- Report prompt processing and generation separately.
- Record runtime, context length and GPU offload.
- Measure sustained power at the wall instead of comparing chip TDP labels.
Complete comparison table
| Criteria | High-memory Apple Silicon | NVIDIA RTX desktop | Winner |
|---|---|---|---|
| Memory for LLM | 36-128 GB (unified) | 24 GB VRAM max | Mac (capacity) |
| Generation speed | Varies by chip, runtime, context and quantization | Often strongest when the full model fits in VRAM | Test |
| 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 (3-8B)
For lightweight quantized models, both platforms work well. A 16GB Apple Silicon Mac or a PC with a 12GB NVIDIA GPU is a practical entry point, provided you leave memory for context and the operating system.
- MacBook Air M4 16GB: silent and portable, with shared memory for model and context
- PC + RTX 3060 12GB: older but still useful when CUDA support and price matter
For medium models (13-30B)
This is where Apple unified memory becomes decisive.
- Apple Silicon with 36-48GB unified memory: enough capacity for many 27-32B Q4-class workloads with sensible context
- PC + RTX 4090 24GB: strong CUDA performance for models that fit its VRAM; larger models can use partial CPU/system-RAM offload at lower speed
For large models (70B+)
High-memory Apple Silicon is the simplest single-box path, while NVIDIA users can choose 32-48GB professional cards or multi-GPU systems when CUDA performance matters more than simplicity.
- High-memory Mac Studio: can load 70B Q4-class models while retaining substantial context headroom; current configurations extend far beyond 128GB unified memory
- NVIDIA alternatives: 32GB consumer or 48GB professional GPUs, with multi-GPU and CPU offload available at greater cost and complexity
Verdict by usage:
- Mobile/developer usage: MacBook Pro M3 — silence, battery, memory capacity
- Pure performance / Gaming: PC NVIDIA — speed, CUDA ecosystem
- Large 70B+ models: high-memory Mac Studio for simplicity; large-VRAM or multi-GPU NVIDIA for CUDA throughput
- 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.