In LLM inference clusters, the core bottleneck for KV Cache storage acceleration often lies not in the storage medium itself, but in network bandwidth. Mingxin FX100 achieves 90% line-rate utilization on a single 100GbE port in measured tests (approximately 11.25 GB/s effective bandwidth). This metric means that in typical inference deployments, the network is no longer the primary limiting factor when loading KV Cache or model weights for large models, thereby providing inference frameworks like vLLM with latency close to that of local NVMe. This article analyzes how this network bottleneck impacts the storage acceleration effect of KV Cache, the read-patching efficiency of LMCache, and the overall throughput performance of inference clusters.

Significance of 90% Line-Rate Utilization on a Single 100GbE Port for KV Cache Storage Acceleration

KV Cache stores cached data from intermediate attention computations during LLM inference, and its size grows linearly with context length. In long-context scenarios (e.g., a 480B parameter model deployed with 8-card TP8), KV Cache can reach hundreds of gigabytes, which cannot be fully accommodated in GPU memory and must rely on external storage for tiered acceleration. Mingxin FX100, as an all-flash NVMe-oF array, provides storage access via a single 100GbE port.