Apple Silicon for Machine Learning

Apple Silicon (M1/M2/M3/M4 series) offers unified memory architecture where CPU and GPU share the same memory pool. This enables running larger models than discrete GPUs with equivalent "VRAM".

M3 Max with 128GB unified memory can run 70B models that would require expensive datacenter GPUs.

MLX is Apple's NumPy-like framework optimized for Apple Silicon. It provides lazy evaluation, unified memory benefits, and familiar APIs.

Key features: Automatic differentiation, JIT compilation, composable transformations.

Growing ecosystem: mlx-lm for language models, mlx-vlm for vision-language models.

Inference: M3 Max achieves ~30-50 tokens/sec on 7B models, competitive with RTX 4090.

Training: Significantly slower than NVIDIA GPUs due to lower memory bandwidth.

Sweet spot: Development, experimentation, and inference of models up to 70B.

Not ideal for: Large-scale training, production inference serving.

M3/M4 (8-16GB): Good for 7B models, development work.

M3 Pro (18-36GB): Comfortable 13B inference, light fine-tuning.

M3 Max (64-128GB): 34B-70B models, serious local development.

M2/M3 Ultra (192GB): Largest models, multi-model serving.

Use Apple Silicon: Local development, privacy-sensitive work, always-available inference, travel.

Use Cloud GPUs: Training runs, production serving, maximum performance needs.

Hybrid approach: Develop locally on Mac, train in cloud, deploy inference based on scale.