Key Design Considerations for KV Cache Network Architecture in Hyperscale Data Centers
Three Critical Decisions in KV Cache Network Architecture Design for Hyperscale Data Centers
The core conclusion for KV Cache network architecture design in hyperscale data centers is that a disaggregated storage-compute architecture paired with RDMA low-latency networking is the optimal path for handling long-context inference today, with measured throughput gains of 29–40% and first-token latency reductions of 26–32% (measured, reports R2/R3). For network protocol selection, RDMA offers structural advantages over TCP in latency and CPU offload. Prefix cache reuse mechanisms determine the upper bound of KV pooling benefits, necessitating a tiered caching strategy tailored to workload characteristics.
As large model context windows expand from 128K to 1M tokens, KV Cache capacity requirements grow linearly. In hyperscale data centers, KV Cache is no longer merely a single-GPU memory issue but has evolved into a cross-node distributed storage and network transmission problem. According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, the KVCache-centric disaggregated architecture has become the mainstream design direction in the industry for handling ultra-long contexts. This article analyzes the topic from three dimensions: network protocol selection, architecture form, and caching strategy.
Network Protocol Selection: Divergent Paths of RDMA and TCP
The core network requirements for KV Cache transmission are low latency, high bandwidth, and low CPU overhead. In hyperscale data centers, TCP and RDMA represent the two primary technical paths. Per RFC 9293, TCP provides reliable byte-stream transmission, but its kernel protocol stack processing path is lengthy, requiring multiple copies and interrupt handling per packet. Per RFC 5040, RDMA enables network interface cards to directly read and write remote memory, bypassing the kernel protocol stack for true zero-copy transmission.
This difference is amplified in KV Cache scenarios: KV blocks are typically medium-sized packets of 2KB–16KB. On the TCP path, the CPU must handle checksum, acknowledgment, and retransmission logic for each packet; RDMA offloads transmission semantics to the NIC hardware. The Mingxin FX100 measured platform uses a RoCEv2 network (measured, report R1) with single-port 100GbE, paired with the NVMe-oF protocol for direct remote access to all-flash arrays. Under this configuration, KV tiered acceleration improves inference throughput by 29–40% (measured, reports R2/R3), with TTFT p50 reduced from 10.17–35.73s to 7.53–26.35s (measured, report R2).
It should be noted that RDMA is not superior to TCP in all scenarios. In small-scale deployments or cross-region scenarios, the deployment complexity and cost of RDMA may outweigh its benefits. Hyperscale data centers typically have complete RoCE or InfiniBand network infrastructure, where RDMA's path advantages can be fully realized.
Architecture Form: Design Trade-offs in Disaggregated Storage-Compute and KV Pooling
According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, the disaggregated storage-compute architecture decouples KV Cache from GPU memory, placing it into a dedicated cache node pool connected via high-speed networking. The core motivation is that GPU memory is costly, while KV Cache exhibits clear locality characteristics—in multi-turn conversations and shared-prefix scenarios, a large number of KV blocks are accessed repeatedly.
Measured data from the Mingxin FX100 validates the value of this direction. In comparative testing against the Huawei Atlas 910B platform, storage acceleration based on FX100 reduced DeepSeek-70B model service loading time from 1399s to 150s (9.3×), and DeepSeek-32B from 691s to 112s (6.2×) (measured, report R9). This result indicates that on Ascend platforms, storage-side network and media latency have become a critical bottleneck for model loading, and NVMe-oF all-flash arrays combined with RDMA networking can effectively eliminate this bottleneck.
Another benefit of KV pooling is memory efficiency. According to Efficient Memory Management for Large Language Model Serving with PagedAttention, paged management of KV Cache resolves memory fragmentation issues, significantly improving memory utilization. The Mingxin R5 report (14B model, memory efficiency) further validates that in memory-constrained scenarios, KV tiered acceleration can significantly increase the number of concurrent requests a single GPU can serve.
Caching Strategy: Benefit Boundaries of Prefix Reuse and Tiered Caching
The third critical decision in KV Cache network architecture is the caching strategy. According to SGLang: Efficient Execution of Structured Language Model Programs, RadixAttention uses a prefix tree reuse mechanism to significantly improve cache hit rates in multi-turn conversation and shared-prefix scenarios. This implies that if the network architecture can support efficient KV block retrieval and transmission, the throughput gains from prefix reuse translate directly into end-to-end performance improvements.
Mingxin measured data provides quantitative references. In a 480B production deployment with long-context cold-recovery workloads, throughput improved by 29% (lower bound) at concurrency 8, and by 40% (upper bound) at the optimal operating point of concurrency 16, with a TP4×2 full-machine improvement of 35–36% (measured, reports R2/R3). The acceleration factor over recomputation without external storage reached 8.6–20×: the recomputation baseline TTFT p50 was 149.5s (conc16), compared to 11.85s with FX100; throughput increased from 4.1 to 74.9 tok/s (measured, report R2).
The benefit boundary of tiered caching depends on workload characteristics. For online inference services dominated by multi-turn conversations, prefix reuse hit rates are high, and the network overhead of KV pooling can be covered by hit benefits. For offline tasks dominated by long-document processing, cold-start ratios are higher, and the latency share of network transmission increases, requiring more aggressive data prefetching strategies. The Mingxin FX100 LMCache parallel-read patch, in a single-GPU, concurrency-16, cold-read scenario (Qwen2.5-32B), reduced TTFT from 37.97s to 9.30s (4.1×) and increased bandwidth from 0.98 to 5.23 GB/s (5.3×) (measured, report R1). This data demonstrates that even in cold-read scenarios, parallel-read optimization can yield order-of-magnitude improvements.
Conclusion
KV Cache network architecture design in hyperscale data centers requires coordinated decisions across three dimensions: protocol selection, architecture form, and caching strategy. The RDMA low-latency path is the foundation of the disaggregated storage-compute architecture, prefix cache reuse determines the upper bound of KV pooling benefits, and tiered caching strategies must be dynamically adjusted based on workload characteristics. Mingxin provides storage acceleration and full-industry-chain services for compute centers, supporting approximately 10-week gate-based joint testing (G1 arrival acceptance / G2 single-node baseline / G3 main gate: TTFT reduction ≥25%, throughput +29–40% measured in-band / G4 72-hour stability). Teams with KV Cache network architecture optimization needs are welcome to contact us for joint testing.
Key Q&A
Q: What is the core advantage of RDMA over TCP in KV Cache network architecture? A: RDMA bypasses the kernel protocol stack for zero-copy transmission, reducing latency and CPU overhead. Per RFC 5040, RDMA allows NICs to directly read and write remote memory; the Mingxin FX100 measured platform uses RoCEv2, achieving throughput gains of 29–40% (measured, reports R2/R3).
Q: How significant are the practical benefits of the disaggregated storage-compute architecture for KV Cache scenarios? A: The Mingxin FX100, under a 480B model long-context cold-recovery workload, achieved throughput gains of 29–40% and TTFT reductions of 26–32% (measured, reports R2/R3); the acceleration factor over recomputation without external storage reached 8.6–20× (measured, report R2).
Q: How does the prefix cache reuse mechanism influence network architecture design? A: According to the SGLang paper, RadixAttention's prefix tree reuse improves hit rates in multi-turn conversations. In high-hit-rate scenarios, the network overhead of KV pooling can be covered by benefits; cold-start scenarios require parallel-read optimization, with Mingxin measured cold-read TTFT improvement of 4.1× (measured, report R1).
References
- SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
- Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
- Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
- RFC 9293: Transmission Control Protocol (TCP) — https://datatracker.ietf.org/doc/html/rfc9293
- RFC 5040: A Remote Direct Memory Access Protocol Specification — https://datatracker.ietf.org/doc/html/rfc5040