Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine
AWS solves the KV cache dilemma: distributed NVMe pooling cuts inference costs without sacrificing speed.
Why it matters
Tiered KV caching on SageMaker HyperPod addresses a real operational pain point for LLM inference at scale — the choice between oversized GPUs or latency. This engineering pattern could reshape how practitioners budget and configure inference clusters.
The key facts
9 to knowTiered KV cache extends into shared distributed NVMe pool
Uses Curvine for near-local-disk-speed cache reuse
Deployed on Amazon SageMaker HyperPod
Targets cost-efficient instances without GPU oversizing
Solves time-to-first-token vs. instance size trade-off
Tiered KV cache extends from GPU to distributed NVMe pool
Uses Curvine for cache management across replicas
Targets cost-efficiency without time-to-first-token degradation
Solves classic inference trade-off: instance size vs. latency
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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 reuse cache at…
