Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance. The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.

Intel's Gaudi 3 AI accelerator offers a powerful, energy-efficient solution for generative AI, providing flexibility, scalability, and cost savings. This innovative technology supports diverse AI workloads and works with open standards based-systems, enabling organizations to maximize ROI and future-proof their AI infrastructure.

A brief history of digital twins and their role in training, simulating, planning, researching and testing complex AI/ML models at massive scale.

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Discover how leveraging the cloud can help organizations accelerate AI adoption.

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Learn about the components of high-performance networking, including available transport technologies and the secret to making things go fast.

Looking for a flexible, cloud-based artificial intelligence solution to complement your Contact Center environment? Google CCAI might be for you.