Enterprise AI
Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine
Optimizing LLM inference costs using shared NVMe storage and distributed caching on Amazon SageMaker.
Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that extends the cache into a shared, distributed NVMe pool with Curvine, so replicas ...
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AWS Machine Learning Blog
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AWS Machine Learning Blog
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Tiered KV caching solves the critical memory bottleneck in large-scale LLM deployment by offloading state to shared NVMe, significantly lowering infrastructure costs. This architecture enables efficient, low-latency inference on cost-optimized hardware without compromising performance at scale.
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