gpu training and inference

The tag gpu training and inference covers discussions about deploying and managing GPU-intensive AI workloads, particularly in cloud environments like Azure. Topics include strategies for balancing portability and vendor lock-in, with emphasis on keeping large GPU training and latency-sensitive inference workloads flexible across platforms. The content advises selective trust in Azure for Microsoft-adjacent AI tasks while designing non-Microsoft-dependent AI systems for portability. Recurring themes involve enterprise IT decision-making, cloud capacity planning, and avoiding single points of failure for GPU-heavy operations. The tag is relevant for professionals evaluating cloud infrastructure for AI model training and real-time inference.