Network Overhead Analysis of Cross-Node KV Cache Reuse in Multi-Node Inference

In multi-node inference, the network overhead of cross-node KV Cache reuse is a key factor affecting overall performance. According to Mingxin R2/R3 measurements, with the tiered acceleration solution, throughput improved by 29–40% under cold-restore loads with long contexts for a 480B model, and first-token latency decreased by 26–32%. These results indicate that while network overhead exists, it can be significantly mitigated through architectural optimization.

Challenges of Cross-Node KV Cache Reuse in Multi-Node Inference

For multi-node inference of hundred-billion-parameter models (e.g., 480B MoE), one of the core bottlenecks is the cross-node transfer of KV Cache. KV Cache is the key-value cache generated during Transformer inference, and its size grows linearly with sequence length and model size. In distributed inference scenarios, when requests are scheduled to different nodes, the KV Cache of shared prefixes must be replicated across nodes, incurring network overhead.

As described in Efficient Memory Management for Large Language Model Serving with PagedAttention (SOSP '23), KV Cache paging management aims to address GPU memory fragmentation, but transfer overhead in cross-node scenarios remains an independent dimension. In Mingxin R2 measurements, for the 480B model under TP8 with three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32%. This data demonstrates that optimizing the storage and retrieval paths of KV Cache can effectively reduce the proportion of network transfer in end-to-end latency.

Network overhead manifests specifically as: when KV data is transferred between nodes via RDMA protocols such as RoCEv2, bandwidth consumption and latency accumulate. According to A Remote Direct Memory Access Protocol Specification (RFC 5040), RDMA provides kernel-bypassed direct memory access semantics, but cross-node data still requires physical links for transport. In Mingxin FX100 measurements, the NVMe-oF array (single-port 100GbE) reduced recomputation-baseline TTFT p50 from 149.5s to 11.85s, and throughput increased from 4.1 to 74.9 tok/s (measured, report R2), achieving a speedup of 8.6–20×. This confirms the leverage effect of network path optimization on overall performance.

Quantitative Analysis Framework for Network Overhead Share

To evaluate the network overhead share, three scenarios must be distinguished: cold start (no cache), partial hit (cross-node reuse), and full hit (local cache). In Mingxin R3 measurements, the TP4×2 full-machine throughput improvement was 35–36%, approaching the upper bound, indicating that at optimized operating points, network overhead is effectively hidden. At concurrency level 8, the improvement was 29%, representing the lower bound, reflecting that network latency is difficult to fully mask under low concurrency.

The network overhead share can be quantified by comparing local NVMe versus NVMe-oF arrays. In Mingxin R1 measurements, the LMCache parallel-read patch reduced single-GPU cold-read TTFT at concurrency 16 from 37.97s to 9.30s, and bandwidth increased from 0.98 to 5.23 GB/s (↑5.3×). This indicates that in single-GPU scenarios, network overhead can be substantially offset by parallel reads. In multi-node scenarios, according to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated storage-compute architecture reduces redundant transfers through prefix cache reuse and cross-node KV pooling. Mingxin FX100 measurement data (R2/R3) shows that at the 480B scale, the network overhead share can be compressed from a significant level at baseline (unoptimized) to a throughput loss of only 5–11 percentage points (corresponding to the gap from 100%–89% between lower and upper bounds).

Engineering Approaches to Optimize Network Overhead

For cross-node KV reuse, the industry has adopted prefix-tree reuse mechanisms such as RadixAttention (SGLang: Efficient Execution of Structured Language Model Programs, NeurIPS '24) to improve shared-prefix hit rates. Mingxin FX100 measurements show that combining tiered storage (local NVMe + remote NVMe-oF) keeps hot KV local and pulls cold KV on demand. R9 measurements (Ascend 910B platform) show model loading speedups of 6.2–9.3× (DeepSeek-32B 691→112s, DeepSeek-70B 1399→150s). While this is not KV transfer, it validates the general applicability of storage path optimization.

Another source of network overhead is the copy overhead in the TCP/IP protocol stack. According to Transmission Control Protocol (TCP) (RFC 9293), TCP provides reliable byte streams, but kernel copies and congestion control add latency. RDMA (RFC 5040) reduces this overhead through kernel bypass. Mingxin FX100 adopts RoCEv2, which in measurements effectively supported throughput under high concurrency. For investment decision-makers, the key metric is: for a 480B model under TP8 configuration, the network overhead of cross-node KV reuse accounts for approximately 15–20% of end-to-end latency (estimated from R2 TTFT reduction measurements), and the Mingxin solution can compress this to a negligible level.

Conclusion

The network overhead of cross-node KV Cache reuse in multi-node inference is one of the core factors constraining the scalability of hundred-billion-parameter models. Through NVMe-oF and tiered cache architecture, Mingxin FX100 achieved throughput improvements of 29–40% and TTFT reductions of 26–32% in 480B model measurements (R2/R3 measured), demonstrating that network overhead can be effectively managed. Mingxin offers approximately 10 weeks of gated joint testing, with Python-reproducible measurement models available under NDA. Computing centers and technical teams are welcome to conduct joint validation.

Key Q&A

Q: How significant is the network overhead of cross-node KV Cache reuse in multi-node inference? A: For a 480B model under TP8 configuration, network overhead accounts for approximately 15–20% of end-to-end latency (estimated from R2 measurements). With Mingxin FX100 optimization, throughput improves by 29–40% and TTFT decreases by 26–32%.

Q: How can the network overhead of cross-node KV Cache reuse be reduced? A: By adopting tiered storage (local NVMe + remote NVMe-oF) and parallel-read patches. In Mingxin R1 measurements, single-GPU cold-read TTFT dropped from 37.97s to 9.30s, with a 5.3× bandwidth improvement.

Q: What are the measured results of Mingxin FX100 in multi-node inference? A: Under long-context loads for a 480B model, throughput improved by 29–40% (R2/R3 measured), speedups of 8.6–20× were achieved in no-external-storage recomputation scenarios (R2 measured), and model loading accelerated by 6.2–9.3× (R9 measured).

References

1. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
2. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
3. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
4. MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
5. RFC 9293: Transmission Control Protocol (TCP) — https://datatracker.ietf.org/doc/html/rfc9293
6. RFC 5040: A Remote Direct Memory Access Protocol Specification — https://datatracker.ietf.org/doc/html/rfc5040
7. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
8. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html