Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine

Summary

AWS has published a technical blog post detailing a tiered KV cache architecture for large language model inference on Amazon SageMaker HyperPod. The solution integrates Curvine, a distributed NVMe storage layer, to extend the KV cache beyond GPU memory into a shared pool. This allows multiple model replicas to reuse cached key-value data at near-local-disk speeds, reducing reliance on expensive oversized GPU instances.

Why it matters

KV cache management is one of the most pressing bottlenecks in production LLM deployment. As model sizes grow, the memory required to store KV cache for each request can dominate GPU utilization, forcing organizations to over-provision hardware or accept degraded latency. AWS's approach directly tackles this by decoupling cache storage from GPU memory, enabling cost-efficient instances to serve large models without sacrificing response speed. This is particularly relevant for enterprises running high-throughput inference workloads where GPU costs represent a significant portion of the inference budget.

Related tools

• SageMaker HyperPod
• Curvine

Impact on AI tools/models

This architecture could reshape how large models are deployed in production. By allowing replicas to share a distributed KV cache, AWS is effectively turning cache reuse into a first-class optimization — similar to how shared memory pools work in database systems. Models that benefit most are those with long context windows and high request concurrency, where KV cache eviction and recomputation currently impose heavy costs. The approach also opens the door for heterogeneous GPU fleets, where cheaper instances can participate in inference clusters without being bottlenecked by cache capacity.

What to watch

• How SageMaker HyperPod evolves as a managed offering for large-scale inference
• Whether Curvine will be adopted beyond AWS for general-purpose distributed caching
• The broader AI tools landscape for production LLM optimization
• Updates to AWS rankings for inference infrastructure

FAQ

What problem does the tiered KV cache solve? It addresses the trade-off between oversized GPU instances and slow time-to-first-token in large LLM inference by extending the KV cache into shared NVMe storage.

What is Curvine used for in this solution? Curvine provides the shared, distributed NVMe pool that allows LLM replicas to reuse cached key-value data at near-local-disk speeds on cost-efficient instances.

Which AWS service is this built on? The tiered KV cache is built on Amazon SageMaker HyperPod.