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You're developing a real-time multimodal A1 system that processes live video and audio streams. The system's performance is lagging behind requirements. Which of the following optimization strategies would be MOST effective in improving the system's throughput and reducing latency?
Correct Answer: B,D,E
TensorRT optimizes models for NVIDIA GPUs, significantly improving inference speed and reducing latency. Mixed-precision inference reduces memory bandwidth requirements and allows for faster computations on Tensor Cores. Reducing the number of layers decreases the computational complexity of the model. RNNs are not typically faster than Transformers, especially when optimized with libraries like TensorRT. While a larger batch size can increase throughput, it also increases latency, which is detrimental for a real-time system.