Enterprise environments are adopting distributed architectures for AI workloads requiring long context windows. As these scale, KV caches exceed local CPU RAM and SSD limits. While pooling local SSDs offers capacity, it adds complexity in data distribution and replication. An alternative is to offload attention states to a high-performance external parallel filesystem like Google Cloud Managed Lustre. This approach bypasses host capacity limits and avoids networking overhead associated with pooled drives. Google Cloud Managed Lustre demonstrably saves TCO and reduces GPU-hour requirements for large language model inference. The solution can be extended with CPU RAM offload for further performance improvements. Key architectural components include GKE GPU nodes and Managed Lustre acting as a decentralized cache. A PVC Evictor manages storage by removing least-recently-used cache chunks. Deployment involves creating a GKE cluster, provisioning Lustre storage, deploying the vLLM serving engine with Lustre integration, and finally deploying the PVC Evictor. Proper Google Cloud project configuration, including quotas and IAM permissions, is essential before deployment.