Essential metrics for AI hardware management

Compute metrics are at the heart of AI infrastructure. ML and AI depend on advanced processors, including GPUs, tensor processing units (TPUs) and neural processing units for high-performance mathematical operations. Processors, memory and model use define AI performance and corresponding cost efficiency.

A cross-section of important metrics for AI compute characteristics includes the following:

• GPU memory saturation occurs when video random access memory (VRAM) on the GPU subsystem is fully used and the GPU then must access slower system RAM across suboptimal interfaces, such as the PCIe bus. Saturation will cause severe performance degradation and result in out-of-memory errors during ML training and inference. GPU memory should generally not exceed 60%-80% utilization to ensure adequate space for model parameters, gradients and other data elements.
• GPU memory bandwidth is the rate of data transfer between a GPU and its VRAM, which provides a high-speed workspace for storing GPU data. The metric is measured in gigabytes per second (GB/s) or terabytes per second. The critical issue is to ensure enough bandwidth where VRAM data can flow without disruption. If data can’t feed the GPUs fast enough, it pauses while it waits for the VRAM to catch up. Inadequate GPU memory bandwidth will slow ML training and inference.
• GPU memory capacity is the total amount of memory available to the GPUs measured in GBs. Capacity is typically coupled with saturation metrics to gauge overall GPU memory use. If utilization regularly reaches saturation, additional memory capacity might be justified. In 2026, GPU memory capacity varies from 16 GB to 140 GB per GPU depending on the workload and AI infrastructure.
• GPU utilization is the percentage of time that a GPU or other processor accelerator technology is active. The goal is to see moderately high levels of use. Typical GPU utilization characteristics include 80% to 95% for training cycles, and 70% to 90% for ongoing inference. Lower percentages suggest that the GPU is underutilized, reducing the cost-effectiveness of the GPU investment. Higher percentages mean that the GPU might be overutilized and could become a bottleneck to AI performance, so additional capacity might be justified.
• Hardware memory error rates, such as bit flips, are common and almost all are readily correctable on the fly. However, AI creates enormous stress on high-performance memory subsystems and interconnects, and heavy data transfer loads can often drive higher hardware memory errors. Each error delays data transfers and processing while it’s corrected or, if left uncorrected, while it reprocesses. Tracking memory error rates and throughput metrics can help infrastructure experts optimize compute performance, identify unreliable memory subsystems and can justify the investment in high-quality memory using advanced error correction techniques.
• ML model latency is the amount of time for a model to process a request and return a response. It’s typically measured in milliseconds per request. Throughput and latency are often used together as an overall measure of model efficiency. For example, high throughput and low latency suggest that the model is operating at high efficiency.
• ML model throughput provides a measure of the model’s performance by determining the number of requests a model can handle per second. It can be measured in many places. Falling throughput suggests the presence of bottlenecks, unoptimized or poorly configured infrastructure.
• Model floating-point operations per second (FLOPS) utilization is a popular standard for training efficiency. MFU is the ratio of achieved theoretical compute performance, measured in billions of FLOPS or TFLOPs to peak hardware capacity. Ideally, the model achieves full utilization of hardware capacity. In actual practice, however, an MFU of 40% or better typically indicates a healthy, well-performing model.