INFINITIX
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From smart customer service and predictive analytics to Generative AI applications, businesses across every sector are aggressively pursuing AI transformation. However, before deployment even begins, many organizations get stuck at
As the GenAI wave rolls on, enterprise demand for GPU compute power is skyrocketing. However, businesses face three critical challenges: managing heterogeneous GPU brands, unequal resource allocation, and a lack
With the rise of Generative AI and deep learning, the demand for GPU compute power from enterprises and research institutions has sharply increased. However, a "resource polarization" occurs: some organizations
With the rapid development of large AI models, the computational resources and costs required to train and deploy these models have escalated dramatically. Facing massive resource demands, enterprises require a
A growing number of enterprises recognize the importance of adopting AI. However, traditional AI projects—from complex model development and challenging training processes to deployment, maintenance, and updates—often face huge resource
INFINITIX has seamlessly integrated Elastic Distributed Training into AI-Stack, supporting mainstream frameworks like Horovod, DeepSpeed, Megatron-LM, and Slurm. In this article, we will provide a step-by-step demonstration of how to
As AI applications become increasingly diverse, the scale of deep learning models is also growing rapidly. From language models and visual recognition to generative AI, the compute resources required to
HPC, which stands for "High-Performance Computing," refers to gathering a large amount of computing resources to process computational tasks that are too massive or complex to run on a typical
Many developers still encounter issues with GPU resource partitioning when using Kubeflow. This article will guide you step-by-step on how to perform Kubeflow GPU partitioning using Infinitix's ixGPU module.