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 the first hurdle: Should we choose cloud-based infrastructure or an on-premises solution?
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 of core visibility. This article will guide you on how enterprises can effectively manage their GPU resources.
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 invest heavily in purchasing high-end GPUs for AI projects, only to see significant idle time during off-peak periods; conversely, many developers and small to medium-sized enterprises (SMEs) are unable to access the necessary compute power due to prohibitive hardware costs. To resolve this contradiction, GPU-as-a-Service (GaaS) emerged.
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 more precise and flexible approach to computing and resource management to boost operational efficiency and control expenditures.
Against this backdrop, the concept of Token-as-a-Service (TaaS) has emerged, offering enterprises a more flexible and transparent scheme for consuming AI compute resources through a usage-based, tokenized billing model.
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 investment, extremely high professional barriers, and cumbersome operation and maintenance (O&M). These hurdles make it difficult for many companies to translate AI capabilities into real business value quickly. It is against this backdrop that Model as a Service (MaaS) emerged.
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 use Horovod for Elastic Distributed Training on AI-Stack!
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 train large models are experiencing explosive growth. Amid this trend, Elastic Distributed Training is gradually becoming a crucial and indispensable technology in the AI development process.
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 desktop computer or workstation.
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.