What should be done to alleviate the issue of some GPUs being overburdened while others are underutilized?

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Implementing dynamic GPU resource allocation based on workload demands is the most effective approach to balance the workload across GPUs. This strategy allows for real-time adjustments to resources depending on current task requirements, ensuring that no single GPU becomes overwhelmed while others sit idle.

With dynamic allocation, workloads can be directed to the GPU with the most available capacity at any given moment. This flexibility not only optimizes processing power but also enhances overall system performance and efficiency, resulting in better resource usage.

Increasing the number of GPUs available can potentially address the issue, but it may not solve the underlying imbalance of workload distribution. Merely adding more hardware does not guarantee that workloads will be effectively balanced across all resources.

Distributing workloads evenly based on historical data might seem reasonable, but it does not account for the variability in workload demands that can change over time. Sometimes, specific tasks may require more GPU power than what historical data suggests, leading to some GPUs being overburdened regardless.

Ensuring all GPUs are of similar capabilities can aid in achieving a more balanced system, but it alone does not directly resolve the issue of uneven workload distribution. It may improve performance but does not address how efficiently workloads are allocated to the GPUs, which is critical for optimal performance.

The key

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