Infrastructure for the future AI: managing the GPU for the AI pipeline

The foundational transformation of AI Infrastructure

Recent research by more than 2,000 business leaders found that for AI to significantly affect the business model, it must be based on infrastructure that has been built with a purpose in mind. In fact, inadequate infrastructure was shown to be one of the primary causes of the failure of AI programs, and it continues to limit progress in more than two-thirds of businesses. However, cost, a lack of well-defined objectives, and the extreme complexity of legacy data environments and infrastructure were the key barriers to a more AI-centric infrastructure.

AI-focused for specific applications

Additionally, it looks like the infrastructure will be customized not just for one type of AI but rather for all of them. For instance, as highlighted by NVIDIA, network fabrics and the cutting-edge software needed to manage them will prove vital for natural language processing (NLP), which requires massive computing power to handle huge amounts of data. Moreover, the goal is to coordinate the activities of huge, highly scaled, and widely dispersed data resources in order to streamline workflows and make sure that projects can be finished on time and within budget.

AI-Stack aims to bring full utilization of GPU for AI-focused orchestration and infrastructure platforms

The efficient allocation and use of GPU resources that are being utilized to compute AI models are one of the biggest challenges in AI development. Companies want to make the process as streamlined and cost-efficient as feasible because running certain workloads on GPUs is expensive. Customers do not want to purchase additional GPU resources just because they think it’s fully utilized of what they already have. Instead, they want to be able to predict when they will be fully utilizing what they have before adding more.

AI Infrastructure for Future AI: InfinitiesSoft AI-Stack

Peak benefits and efficiency of using InfinitiesSoft AI-Stack

Its Kubernetes-based orchestration tools enable AI research to accelerate, innovate and complete AI initiatives faster. The platform provides IT and MLOps with visibility and control over job scheduling and dynamic GPU resource provisioning.

  1. Scheduling: fair scheduling, an automatic super scheduler enables users to quickly and automatically allocate GPUs among the cluster’s jobs based on work priority, queuing, and predetermined GPU quotas.
  2. Distributed Training: using multi-GPU distributed training to shorten training durations for AI models.
  3. GPU Sharing: multiple containers can be shared in a single GPU so that multiple users can have their own containers in an operating environment.
  4. Visibility: a simple interface makes it possible to monitor workloads, resource allocation, utilization, and much more. Monitoring utilization at the individual task, cluster, or node level.
An Overview of InfinitiesSoft AI-Stack Platform




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