NOW LET US – AI RAG SaaS Studio TP.HCM
NOW LET US
Digital Product Studio
Back to news
DEV-TOOLS...1 min read

Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter

Share
NOW LET US Article – Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter

Researchers introduce Prefill-as-a-Service (PrfaaS), a cross-datacenter architecture that decouples prefill and decode phases by efficiently transferring KVCache over standard Ethernet, enabling independent scaling and higher throughput.

Computer Science > Distributed, Parallel, and Cluster Computing

Title:Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter

View PDF HTML (experimental)Abstract:Prefill-decode (PD) disaggregation has become the standard architecture for large-scale LLM serving, but in practice its deployment boundary is still determined by KVCache transfer. In conventional dense-attention models, prefill generates huge KVCache traffics that keep prefill and decode tightly coupled within a single high-bandwidth network domain, limiting heterogeneous deployment and resource elasticity. Recent hybrid-attention architectures substantially reduce KVCache size, making cross-cluster KVCache transport increasingly plausible. However, smaller KVCache alone does not make heterogeneous cross-datacenter PD serving practical: real workloads remain bursty, request lengths are highly skewed, prefix caches are unevenly distributed, and inter-cluster bandwidth fluctuates. A naive design that fully externalizes prefill can therefore still suffer from congestion, unstable queueing, and poor utilization.

We present Prefill-as-a-Service (PrfaaS), a cross-datacenter serving architecture that selectively offloads long-context prefill to standalone, compute-dense prefill clusters and transfers the resulting KVCache over commodity Ethernet to local PD clusters for decode. Rather than treating reduced KVCache as sufficient, PrfaaS combines model-side KV efficiency with system-side selective offloading, bandwidth-aware scheduling, and cache-aware request placement. This design removes the requirement that heterogeneous accelerators share the same low-latency RDMA fabric, enabling independent scaling of prefill and decode capacity across loosely coupled clusters. In a case study using an internal 1T-parameter hybrid model, a PrfaaS-augmented heterogeneous deployment achieves 54% and 32% higher serving throughput than homogeneous PD and naive heterogeneous baselines, respectively, while consuming only modest cross-datacenter bandwidth.

© 2026 Now Let Us. All rights reserved.

Source: Hacker News

Advertisement
Ad slot ready: 5887729102

More in this category

EXPLORE TOPICS

Discover All Categories

Deep dive into the specific technology sectors that matter most to you.