In the current environment, where Artificial Intelligence (AI) and High-Performance Computing (HPC) are increasingly reliant on powerful hardware, Graphics Processing Units (GPUs) have become critical computing resources in data centers. However, recent reports suggest that the lifespan of these high-cost GPUs in real-world use may be limited to only one to three years, posing a potential economic challenge for the AI industry.
According to an observation cited by Tech Fund from a senior expert at Alphabet, the lifespan of a data center GPU is heavily influenced by its utilization rate. In modern data centers, GPUs are primarily tasked with executing high-intensity computational workloads such as AI training and inference. Under these conditions, GPUs are frequently under heavy load, causing their rate of wear and tear to significantly outpace that of other hardware components. Cloud Service Providers (CSPs) have noted that GPU utilization rates typically hover between 60% and 70% during operation, and this high-load working environment further shortens the expected GPU lifespan.
At such utilization levels, the average lifespan of a GPU generally falls between one and two years, extending to a maximum of three years. While this assertion has not yet been fully substantiated, the fact that modern GPUs often have power consumption reaching 700W or higher places immense stress on the silicon, lending a degree of credibility to this perspective.
To extend the service life of GPUs, reducing their utilization rate is considered an effective measure. However, this approach would also slow the rate of GPU depreciation, subsequently affecting the efficiency of capital recovery—an undesirable outcome for most commercial operations. Consequently, many Cloud Service Providers prefer to maintain a high GPU utilization rate to achieve the optimal return on investment (ROI).
An earlier study conducted by Meta on their Llama 3 405B model training illustrates this challenge. They utilized a cluster composed of 16,384 Nvidia H100 80GB GPUs. Although the model’s Floating Point Unit (FPU) utilization rate (MFU) was approximately 38%, the 54-day training period experienced 419 unforeseen failures. Out of these, 148 failures (about 30.1%) were caused by various GPU malfunctions, including NVLink faults, while 72 failures (about 17.2%) were attributed to HBM3 memory issues. This clearly indicates that GPUs face significant failure risks even at relatively lower utilization rates.
Meta’s research findings suggest that, based on their observed failure rates, the annual failure rate for the H100 GPU is approximately 9%, translating to an estimated 27% cumulative failure rate over three years. However, as GPUs accumulate more service time, the frequency of failures is likely to increase further, presenting substantial operational challenges.
As AI and HPC applications continue to advance, the demand for GPUs in data centers will only keep growing. Yet, the short lifecycle of GPUs imposes severe constraints on data center operations and maintenance. To address this challenge, the industry urgently needs to develop more durable GPU architectures and devise effective methods for managing and extending GPU service life.
Concurrently, data center operators must re-evaluate their hardware refresh strategies to adapt to evolving technological demands and increasing computational loads. Capital investment plans based on the traditional three-year depreciation period are becoming unrealistic, necessitating a shift towards shorter-term investment recovery schedules to manage potential cash flow pressures.