What approach best balances high availability requirements and energy consumption for a large-scale inference service?

Implementing an auto-scaling group of GPUs that adjusts the number of active GPUs based on real-time load effectively balances high availability requirements and energy consumption for a large-scale inference service. This approach allows for dynamic resource allocation, meaning that the system can scale up during peak demand periods to ensure that performance and availability are maintained. Conversely, during times of lower demand, the auto-scaling functionality can reduce the number of active GPUs, thereby conserving energy and reducing operational costs.

This solution is particularly efficient because it adapts to changing usage patterns without sacrificing responsiveness or uptime, which is crucial for inference services that often experience variable workloads. By optimizing resources according to actual demand, organizations can achieve their performance goals while also being mindful of energy consumption and associated costs.

In contrast, options that involve scheduling inference tasks in batches or using a fixed number of GPUs may not respond as efficiently to fluctuating demands. Scheduling may introduce latency during off-peak hours, and a fixed capacity does not take advantage of potential resource savings when the workload is lower. Using a single powerful GPU continuously at full capacity is not energy-efficient as it does not adapt to variable workloads and can lead to wasted energy when demand decreases.