The exponential growth of AI workloads in cloud data centers has prompted a significant shift in how hardware is managed. Traditional general-purpose servers are being replaced with specialized high-performance computing servers tailored for AI tasks. As cloud service providers like Azure, AWS, and GCP invest heavily in GPU, TPU, and NPU servers, they face challenges concerning reliance on original equipment manufacturers (OEMs) for maintenance. The uncertain repair SLAs from OEMs drive providers to adopt in-house management, leading to an urgent need for improved diagnostics and reduced service costs to support robust AI operations.
The growing demand for specialized high-performance computing servers for AI workloads necessitates a shift from OEM reliance to in-house maintenance, enhancing fleet availability.
Cloud providers face challenges with hardware failures impacting AI workloads, which drives the need for reliable, efficient, self-maintaining systems capable of diagnosing issues swiftly.
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