Although proprietary software toolkits like TensorRT offer customization options, they frequently fail to meet this demand. Additionally, AI production pipelines frequently need rapid development.
Because of hardware dependencies in complicated runtime environments, it is challenging to maintain the code that makes up these solutions. A machine learning system created for one company’s GPU must be entirely reimplemented to run on hardware from a different technology vendor. Currently, AI practitioners now have a minimal choice in the matter of choosing high-performance GPU inference solutions due to their platform-specific nature. GPUs are crucial in delivering the computational power required for deploying AI models for large-scale pretrained models in various machine learning domains like computer vision, natural language processing, and multimodal learning.