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Over the past year, AI inferencing has become significantly more resource-intensive due to the exponential growth in the size and capabilities of large language models (LLMs). These models are not only larger but also more capable, powering a wide range of applications from advanced reasoning and instruction-following to highly specialized, domain-specific tasks.
As these workloads grow in both scale and strategic importance, Kubernetes has emerged as the preferred platform for deploying inference services, offering the scalability and…
