Apple Silicon - Local LLM Knowledge Base

Apple Silicon (M1/M2/M3/M4 chips) uses a fundamentally different architecture than discrete GPUs. Instead of separate CPU and GPU memory, Apple uses unified memory that both can access. This enables running models that wouldn't fit in typical GPU VRAM.

The Unified Memory Advantage

Traditional (Discrete GPU): ┌──────────────┐ ┌──────────────┐ │ CPU │ │ GPU │ │ System RAM │◄────►│ VRAM │ │ (64GB+) │ PCIe │ (24GB) │ └──────────────┘ └──────────────┘ Model must fit in VRAM (24GB limit) Apple Silicon (Unified): ┌─────────────────────────────────────┐ │ Unified Memory │ │ (up to 192GB) │ │ │ │ CPU ◄─────────────────► GPU │ │ 800 GB/s │ └─────────────────────────────────────┘ Model can use ALL memory (192GB)

Apple Silicon Specs

Chip	Max Memory	Bandwidth	GPU Cores	Found In
M1	16GB	68 GB/s	7-8	MacBook Air/Pro (2020)
M1 Pro	32GB	200 GB/s	14-16	MacBook Pro 14"/16"
M1 Max	64GB	400 GB/s	24-32	MacBook Pro, Mac Studio
M1 Ultra	128GB	800 GB/s	48-64	Mac Studio
M2	24GB	100 GB/s	8-10	MacBook Air (2022)
M2 Pro	32GB	200 GB/s	16-19	MacBook Pro, Mac Mini
M2 Max	96GB	400 GB/s	30-38	MacBook Pro, Mac Studio
M2 Ultra	192GB	800 GB/s	60-76	Mac Studio, Mac Pro
M3	24GB	100 GB/s	8-10	MacBook Air/Pro
M3 Pro	36GB	150 GB/s	14-18	MacBook Pro
M3 Max	128GB	400 GB/s	30-40	MacBook Pro
M4	32GB	120 GB/s	10	iPad Pro, Mac Mini
M4 Pro	64GB	270 GB/s	20	Mac Mini, MacBook Pro

Performance by Model Size

Model	M2 Ultra (192GB)	M3 Max (128GB)	M4 Pro (64GB)
Llama 3 8B Q4	~100+ tok/s	~80 tok/s	~60 tok/s
Llama 3 70B Q4	~18-22 tok/s	~10-14 tok/s	~8-10 tok/s
Llama 3 70B Q8	~12-15 tok/s	Doesn't fit	Doesn't fit
Mixtral 8x7B Q4	~25-30 tok/s	~15-20 tok/s	~10-12 tok/s

Software Support

llama.cpp + Metal

Best performance. Native Metal backend, actively optimized.

# Run with Metal acceleration
./main -m model.gguf -ngl 99

Ollama

Uses llama.cpp Metal backend. Just works.

ollama run llama3:70b

MLX

Apple's native ML framework. Optimized specifically for Apple Silicon.

pip install mlx-lm
mlx_lm.generate --model mlx-community/Llama-3-70B-Instruct-4bit

PyTorch MPS

PyTorch's Metal Performance Shaders backend. Works but less optimized than llama.cpp or MLX for inference.

Choosing an Apple Machine

What models do you need? │ ├── 7B-13B models │ ├── Mac Mini M4 (~$800): Budget friendly, 32GB │ └── MacBook Air M3 (~$1,500): Portable, 24GB │ ├── 13B-34B models │ ├── Mac Mini M4 Pro 64GB (~$1,800): Best value │ └── MacBook Pro M3 Pro (~$2,500): Portable │ ├── 70B models (Q4) │ ├── MacBook Pro M3 Max 128GB (~$4,500): Portable 70B! │ └── Mac Studio M2 Max 96GB (~$3,000): Desktop │ └── 70B+ or long context └── Mac Studio M2 Ultra 192GB (~$6,000+): Maximum capacity

Apple vs Discrete GPU

Aspect	Apple Silicon	NVIDIA GPU
Max memory	192GB	24GB (consumer)
Bandwidth	Up to 800 GB/s	Up to 1,008 GB/s
Power draw	30-60W typical	300-450W
Noise	Silent to quiet	Loud under load
Cost for 70B	~$4,500+ (M3 Max 128GB)	~$1,600 (2× 3090 used)
Software	Good (llama.cpp, MLX)	Best (CUDA ecosystem)
Other uses	Great general computer	Gaming, rendering

Tips for Apple Silicon

Maximize Performance

Use llama.cpp or MLX for best Metal optimization
Close other apps — unified memory is shared with everything
Monitor memory pressure — swapping kills performance
Keep macOS updated — Metal optimizations improve regularly

Common Pitfalls

Don't compare TFLOPS — Apple chips have lower TFLOPS but unified memory changes the game
Memory pressure — if you see yellow/red in Activity Monitor, the model is too big
Background apps — Safari with many tabs can use 10GB+ of your LLM memory

The Sweet Spot

Apple Silicon shines when:

You need to run models that don't fit in 24GB VRAM
Silence matters (bedroom, office)
You want a great general-purpose computer too
Portability matters (MacBook running 70B on battery)
Power costs matter (60W vs 700W)

NVIDIA wins when:

Models fit in 24GB (faster bandwidth per $)
Budget is tight ($800 used 3090 vs $4,500 Mac)
You need CUDA-specific software
Training or fine-tuning (Apple is inference-only practically)