GPU infrastructure planning in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - GPU infrastructure planning

Which metric matters for GPU infrastructure planning and WHY

When planning GPU infrastructure for machine learning, key metrics include throughput (how many tasks the GPUs can handle per time), latency (how fast each task completes), and utilization (how busy the GPUs are). These metrics help decide how many GPUs are needed and how powerful they should be. For example, high throughput means more models or data can be processed quickly. High utilization means the GPUs are used well without wasting resources.

Confusion matrix or equivalent visualization

GPU planning does not use a confusion matrix like classification models. Instead, visualize resource usage with a GPU utilization chart showing busy vs idle times, or a throughput graph showing tasks completed per second. For example:

    Time (min) | GPU Utilization (%)
    -----------------------------
         0    |  20
         1    |  50
         2    |  90
         3    |  85
         4    |  95
    

This helps see if GPUs are underused or overloaded.

Precision vs Recall tradeoff analogy for GPU planning

Think of precision as avoiding wasted GPU time (not running unnecessary tasks), and recall as making sure all needed tasks get done quickly. If you add too many GPUs, you have high recall (all tasks done fast) but low precision (some GPUs sit idle). If you have too few GPUs, you have high precision (no waste) but low recall (tasks wait too long). The goal is to balance so GPUs are busy but not overloaded.

What good vs bad GPU planning metrics look like

• Good: GPU utilization around 70-90%, throughput meets task demand, latency is low enough for your needs.
• Bad: Utilization below 30% (wasting money), or above 95% (risking slowdowns), throughput too low causing delays, or latency too high for real-time needs.

Common pitfalls in GPU infrastructure planning metrics

• Ignoring peak usage times and only looking at average utilization can hide bottlenecks.
• Not accounting for data transfer times between CPU and GPU, which can slow down tasks.
• Overfitting to current workloads without planning for future growth.
• Confusing high utilization with good performance; sometimes GPUs are busy but slow due to inefficient code.

Self-check question

Your GPU cluster shows 98% utilization but tasks are taking too long to finish. Is this good? Why or why not?

Answer: No, this means GPUs are overloaded. High utilization with slow tasks suggests bottlenecks. You may need more GPUs or optimize code to reduce task time.

Key Result

Effective GPU planning balances utilization (70-90%) and throughput to meet task demands without overload or waste.