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"Why is my video AI only utilizing 15% of CPU and 5% of GPU on an M3 machine?"

**Neural Engine**: The M3 chip has a faster and more efficient 16-core Neural Engine, which is specifically designed for machine learning tasks, but it might not be fully utilized by your video AI software.

**CPU and GPU accelerators**: The M3 chip includes accelerators in the CPU and GPU to boost on-device machine learning, but these accelerators might not be optimized for your specific video AI workload.

**AI compute tasks**: The M3 chip is designed to handle AI compute tasks more efficiently, but it might require specific software optimization to take full advantage of its capabilities.

**GPU cores**: The M3 chip has a variable number of GPU cores, ranging from 14 to 18, which might affect the performance of your video AI software.

**CPU efficiency**: The M3 chip's CPU is optimized for efficiency, which might lead to lower CPU utilization when running AI workloads.

**Video upscaling**: Upscaling video from 480i to 4K is a computationally intensive task, but the M3 chip's AI accelerators might not be fully utilized if the video AI software is not optimized for the M3 architecture.

**Apple Silicon architecture**: The M3 chip is part of Apple's Silicon architecture, which is designed for efficient performance and power management, but might require specific software optimization to take full advantage of its capabilities.

**MLX framework**: The MLX framework is a machine learning framework that can utilize the M3 chip's AI accelerators, but its performance can vary depending on the specific workload.

**AI benchmarking**: Benchmarking AI performance is a complex task, and results can vary depending on the specific workload and software optimization.

**Power management**: The M3 chip is designed to optimize power management, which might lead to lower CPU and GPU utilization when running AI workloads.

**Thermal design power**: The M3 chip has a thermal design power (TDP) of around 15W, which affects its performance and power management.

**GPU performance**: The M3 chip's GPU performance is optimized for machine learning and AI workloads, but its performance can vary depending on the specific workload.

**AI compute tasks parallelization**: The M3 chip's AI accelerators are designed to parallelize AI compute tasks, which can lead to better performance and efficiency.

**Software optimization**: Video AI software needs to be optimized for the M3 chip's architecture to take full advantage of its capabilities, including AI accelerators and power management.

**M3 chip variations**: There are different variations of the M3 chip, including the M3 Pro and M3 Max, which have different CPU and GPU core counts, affecting performance and power management.

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