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?

The correct answer is A. The model is too large to fit into GPU memory. Multi-node training is advantageous when a model's size--its parameters, activations, and gradients-exceeds the memory capacity of a single GPU. By sharding the model across multiple nodes (using techniques like data parallelism or model parallelism), training becomes feasible…

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Question

Options

AThe model is too large to fit into GPU memory.
BThe model is being used by a large number of users.
CThe model is being used for large-scale inference workloads.

How the community answered

(19 responses)

A
95% (18)
C
5% (1)

Explanation

Multi-node training is advantageous when a model's size--its parameters, activations, and gradients-exceeds the memory capacity of a single GPU. By sharding the model across multiple nodes (using techniques like data parallelism or model parallelism), training becomes feasible and efficient. User count and inference scale are unrelated to training architecture needs, which focus on compute and memory distribution.

Topics

#Multi-node training#Distributed training#GPU memory#Model scaling

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