GPU utilization can be a misleading metric

GPU Utilization is commonly used as a primary metric for evaluating GPU performance, but it can be misleading. It only indicates whether a kernel is running, not the efficiency of resource usage. Trainy discovered while assisting a company with LLM training that 100% GPU utilization did not translate into effective computational use. They introduced MFUs, a better performance metric, and explained SM Efficiency, leading to significant optimizations and improvements in the GPU clusters.

• GPU Utilization doesn't account for computation efficiency.
• MFUs give a true picture of the GPU's computational performance.
• SM Efficiency measures active SMs during a kernel execution.
• Optimizations led to a 4x speedup in training time.
• High GPU utilization might not mean effective use of resources.