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Optimized model binaries tailored for specific hardware architectures.
Distinct from Binary Optimizers: Distinct from general binary optimizers: focuses on hardware-specific context binaries for ML inference.
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This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation
Converts models into optimized context binaries tailored for specific hardware architectures.