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Optimizing AI Workloads with NVIDA GPUs, Time Slicing, and Karpenter (Part 2)
Introduction: Overcoming GPU Management Challenges In Part 1 of this blog series, we explored the challenges of hosting large language models (LLMs) on CPU-based workloads within an EKS cluster. We discussed the inefficiencies associated with using CPUs for such tasks, primarily due to the large model sizes and slower inference speeds. The introduction of GPU resources offered a significant performance boost, but it also brought about the need for efficient management of these high-cost resources. …
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