What factors have led to significant breakthroughs in Deep Learning?
Answer(s): C
Breakthroughs in deep learning have been driven by more powerful hardware (e.g., GPUs and TPUs), the emergence of massive curated datasets for training, and continual innovations in training algorithms (optimizers, architectures, regularization) that make it possible to effectively learn deep neural networks.
Which type of GPU core was specifically designed to realistically simulate the lighting of a scene?
Ray Tracing Cores are specialized hardware units designed to accelerate the computation of ray-scene intersections and shading calculations, enabling real-time, photorealistic lighting and reflections.
Which GPUs should be used when training a neural network for self-driving cars?
Answer(s): A
NVIDIA H100 GPUs deliver the massive compute throughput and high-bandwidth memory needed to train the large, compute-intensive neural networks used in autonomous driving.
A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes. Why would multi-node training be advantageous?
Multi-node training lets you shard and distribute model parameters across GPUs on different servers, enabling you to train models that exceed the memory capacity of any single GPU.
When should RoCE be considered to enhance network performance in a multi-node AI computing environment?
RoCE offloads transport work to the NIC via RDMA, cutting CPU overhead and unlocking the full link bandwidth when high CPU usage would otherwise throttle network throughput.
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