Remote KV Cache Offloading: Where Network Latency Begins to Lose Control

Offloading the KV Cache to remote storage in vLLM does not degrade TTFT linearly; instead, a distinct inflection point emerges at a specific network latency threshold. According to Mingxin's measured results, report R2 (480B·TP8 long-context·production release), when network round-trip latency exceeds approximately 200 microseconds, the increase in TTFT begins to deviate significantly from baseline. Above 500 microseconds, the benefits of offloading are entirely negated. This finding offers direct engineering reference value for inference clusters planning a disaggregated storage-compute architecture.

Latency Composition and Degradation Mechanism of Remote KV Cache Offloading

After offloading the KV Cache to remote storage, each inference request's first-token generation path incurs additional network round trips. As described in "Efficient Memory Management for Large Language Model Serving with PagedAttention," the paged management of the KV Cache was originally designed to address GPU memory fragmentation and dynamic growth issues, while remote offloading further extends the storage hierarchy beyond memory. The cost of this architectural choice is that every cache miss requires fetching data from remote storage via RDMA or TCP.

In Mingxin's measured results, report R2, with the 480B model under TP8 configuration, TTFT p50 across three concurrency levels dropped from a local NVMe baseline of 10.17–35.73s to 7.53–26.35s (FX100 offloading scenario). The key point here: at low network latency tiers (e.g., microsecond-level latency of RoCEv2), the benefits of offloading outweigh the overhead; as latency rises, the overhead progressively erodes the gains.

Where Is the Inflection Point: 200 Microseconds as the Dividing Line

Based on Mingxin's measured data, report R2, at concurrency level 8, KV-tiered inference throughput improved by +29% (lower bound), while at the optimal operating point of concurrency 16, the improvement reached +40% (upper bound). This throughput gain presupposes network latency within the typical RoCEv2 range. When network latency exceeds approximately 200 microseconds, the TTFT degradation curve steepens noticeably.

From an engineering perspective, the cause of this inflection point lies in the fact that KV Cache reads are a critical dependency on the request path; the latency of each cache miss directly accumulates into TTFT. According to "RFC 5040: A Remote Direct Memory Access Protocol Specification," one design goal of the RDMA protocol is to reduce end-to-end latency, but in cross-switch and cross-rack scenarios, latency increases with hop count. When a single round trip exceeds 200 microseconds and a request requires multiple reads, the cumulative effect becomes apparent.

TTFT Performance Comparison Across Latency Tiers

For decision-makers to grasp intuitively, the table below summarizes TTFT performance at different concurrency levels from Mingxin's measured results, report R2, along with corresponding network latency sensitivity:

Notably, at concurrency 16, FX100 achieves the largest TTFT improvement (from 17.20s to 11.85s), which is also the operating point where throughput gains reach +40%. When network latency exceeds 500 microseconds, the benefits of offloading are fully offset, and TTFT may even degrade below the local NVMe baseline.

Selection Criteria: When Remote Offloading Is Suitable, When to Stay Local

Based on the measured boundaries above, the following selection criteria can be established:

Scenarios suitable for remote offloading: Controllable network latency (RoCEv2 or InfiniBand environments with microsecond-level single-hop latency), moderate concurrency (around level 16), and long-context cold-recovery workloads. According to "Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving," a KVCache-centric disaggregated storage-compute architecture offers design advantages in cross-node KV pooling scenarios, but only when the network infrastructure provides low-latency guarantees.

Scenarios unsuitable for remote offloading: Network latency exceeding 200 microseconds (e.g., cross-region deployments), extremely low concurrency (latency-sensitive interactive workloads), or strict SLA constraints on TTFT. In these cases, local NVMe or memory-level caching should be prioritized.

The +29–40% throughput improvement and TTFT reduction of ↓26–32% demonstrated by Mingxin FX100 in report R2, measured, were all achieved in a RoCEv2 single-port 100 GbE environment. If your deployment environment has network latency higher than this baseline, it is recommended to validate actual performance during the joint testing phase (approximately 10-week gate-based process) rather than directly applying the figures above.

Key Q&A

Q: At which network latency tier does the TTFT degradation inflection point occur for remote KV Cache offloading? A: According to Mingxin's measured results, report R2, when network round-trip latency exceeds approximately 200 microseconds, the TTFT degradation curve begins to deviate significantly from baseline; above 500 microseconds, offloading benefits are fully negated.

Q: At the optimal operating point, what are FX100's throughput and latency performance? A: At concurrency 16, throughput improvement reaches +40% (upper bound), and TTFT p50 drops from 17.20s to 11.85s, both from Mingxin's measured data, report R2.

Q: Which scenarios are unsuitable for offloading the KV Cache to remote storage? A: Scenarios with network latency exceeding 200 microseconds (e.g., cross-region), extremely low concurrency, or strict SLA constraints on TTFT—in these cases, local NVMe or memory-level caching is recommended.

References

1. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
2. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
3. RFC 5040: A Remote Direct Memory Access Protocol Specification — https://datatracker.ietf.org/doc/html/rfc5040