Inference tasks vary widely in complexity, data size, latency requirements, and parallelism, and each workload type interacts differently with CPU capabilities. Understanding this relationship allows for more effective hardware selection and optimization strategies tailored to specific use cases.
Key Learning Areas -AI Model Architecture -Types of Inference Workloads -Quantization: Balancing Accuracy and Efficiency -Data Throughput and Bandwidth -Benchmarking Inference Performance -Frameworks and Libraries Impact Performance
