A team has successfully pre-trained a 100-billion parameter language model across a cluster of GPUs using a combination of tensor and pipeline parallelism. They are now tasked with deploying this model for a high-throughput, low-latency inference service. Which of the following approaches represents the most sound and efficient strategy for deploying the model?

Evaluating an Inference Deployment Plan

When deploying a large language model that was trained using a distributed setup with pipeline and tensor parallelism, the engineering team must develop entirely new, inference-specific parallelization methods because the computational demands and optimization goals of training and inference are fundamentally different.