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You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?
Correct Answer: A
The 'Instance Group Count' parameter in Triton determines how many instances of the model are loaded onto the GPU(s) and/or CPU(s). Increasing the number of instances (up to the hardware's capacity) allows Triton to handle more concurrent requests in parallel, thereby improving throughput and reducing latency. While batching and max queue size can also help, the instance count is the most fundamental for parallelism. The default model filename is irrelevent to performance and input data type is a requirement not a performance consideration.