apple-silicon
Skill1 starsChanged 50 days agoMITView on GitHub
Apple Silicon optimization for VMs, ML inference, and compression. Covers Metal GPU acceleration, MLX model inference, CoreML conversion, M-series zstd compression, Apple Virtualization Framework, and performance tuning. Use when working on macOS M-series performance, QEMU/VM setup, or squeezing more out of Apple hardware.
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Apple Silicon Optimization Skill
Comprehensive guide for optimizing applications on Apple Silicon (M1/M2/M3/M4) including GPU acceleration, ML inference, virtualization, and compression.
Overview
Apple Silicon's unified memory architecture and specialized hardware enable significant performance gains when properly optimized:
| Component | Capability | Use Case |
| Neural Engine | 16+ TOPS | Quantized model inference |
| Metal GPU | Up to 76 cores | Vector search, embeddings |
| Unified Memory | Up to 192GB | Large model loading |
| ProRes Engine | Hardware decode | Media processing |
References
references/metal-mlx.md— Metal GPU acceleration + MLX inferencereferences/coreml-zstd.md— CoreML conversion + M-series zstd compressionreferences/vf-tuning.md— Apple Virtualization Framework, performance tuning, Datadog, best practices
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