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adapterOS
Apple Silicon-optimized adapter runtime for efficient multi-model AI workloads. Memory-parallel LoRA mixing.
What It Is
Performance optimization layer for running multiple AI models on Apple Silicon. Implements fused kernels for adapter application and memory-parallel LoRA mixing.
Why It Matters
On-device AI requires efficient use of limited hardware. AdapterOS maximizes throughput while minimizing memory footprint, enabling deployment of larger models on edge devices.
Key Features
- Fused Kernel Operations - Reduced memory bandwidth for adapter application
- Dynamic Adapter Swapping - Switch adapters without reloading base model
- Multi-workload Support - Run multiple LoRA adapters simultaneously
- MLX Native - Built directly on Apple's ML framework
- M2/M3 Optimized - Target current-generation Apple Silicon
Technical Details
Core infrastructure powering MLNavigator's on-device inference. Research component explores memory-parallel adapter architectures for improved throughput.
Drop-in replacement for standard LoRA inference with 2-3x performance improvement on typical workloads.