Methodology for Comparing KV Cache Storage Performance in Multi-Turn Dialogue Scenarios

When comparing KV Cache storage products from different vendors in multi-turn dialogue scenarios, the conclusion comes first: horizontal comparisons that ignore workload patterns and test definitions provide no decision-making value. Mingxin's measurements on a 480B production-grade workload show that KV tiered acceleration reduces time-to-first-token (TTFT) by 26–32% and increases inference throughput by 29–40% [measured, reports R2/R3]. However, these results hold only under the specific pattern of long context and multi-instance concurrency. This article provides a reproducible comparison methodology for evaluation teams to verify independently.

Why Multi-Turn Dialogue Is the Litmus Test for KV Cache Storage

The key difference between multi-turn dialogue and single-turn inference lies in the prefix reuse rate. According to Efficient Memory Management for Large Language Model Serving with PagedAttention, the core motivation for paged KV Cache management is GPU memory fragmentation and dynamically growing memory demands—in multi-turn scenarios, historical tokens accumulate continuously, and KV Cache size grows linearly with the number of dialogue turns, far exceeding single-turn requests.

SGLang's RadixAttention mechanism further reveals the value of prefix tree reuse: in multi-turn dialogue, system prompts and historical context form reusable prefix branches [SGLang paper]. Mingxin's test platform (8×AMD MI308X, 480B MoE model) shows that when concurrency rises from 8 to 16, throughput improvement increases from +29% to +40%—the higher the concurrency, the greater the benefit from KV reuse [measured, reports R2/R3].

The Mooncake architecture paper points out that in a KVCache-centric disaggregated design, the core trade-off is offloading KV from GPU memory to remote pooled storage in exchange for higher GPU memory utilization [Mooncake paper]. This is exactly where storage systems enter the picture in multi-turn scenarios: the longer the dialogue turns, the more KV blocks need to be swapped in and out, and the more directly storage latency affects TTFT.

Three Critical Test Definitions for Comparative Evaluation

First, workload patterns must align with production characteristics. Mingxin's R2 report uses a 480B·TP8 configuration with three concurrency levels, where TTFT p50 drops from 10.17–35.73s to 7.53–26.35s [measured, report R2]. If tests use a 14B model with short context and low concurrency, the storage bottleneck never surfaces, and any product can produce impressive numbers—but those do not reflect production pressure. Comparisons should fix three elements: model size, context length, and concurrency level.

Second, distinguish between "speedup multiples" and "absolute latency." Mingxin's speedup over a no-external-memory recomputation baseline is 8.6–20×, with the recomputation baseline TTFT p50 reaching 149.5s (concurrency 16) and FX100 reducing it to 11.85s [measured, report R2]. But the gap between 8.6× and 20× comes primarily from baseline selection—the slower the baseline, the more impressive the multiple. Decision-makers should check whether absolute latency meets SLAs rather than being swayed by multiples.

Third, GPU memory benefits and storage performance must be evaluated separately. Mingxin's R5 report covers GPU memory benefit scenarios for a 14B model, while the R1 report tests LMCache parallel read patches: on a single GPU with concurrency 16 and cold disk reads, TTFT drops from 37.97s to 9.30s (4.1×), and bandwidth improves from 0.98 to 5.23 GB/s (↑5.3×) [measured, report R1]. The former answers "how much GPU memory can be saved," while the latter answers "how fast the storage system itself is"—the two must not be conflated.

Public Basis and Limitations for Cross-Vendor Comparison

The current neutral public basis for comparison is the MLPerf Inference benchmark, whose value lies in fixed precision and latency constraints in the test definition [MLPerf official site]. However, the MLPerf Datacenter scenario primarily covers inference throughput and latency and does not treat external KV Cache storage as an independent variable—meaning it cannot directly answer "whose KV Cache storage solution is faster."

NVIDIA CMX's public positioning provides an architectural reference: according to NVIDIA's official page, CMX is defined as an AI-native context memory storage layer that leverages BlueField-4 and DOCA Memos for prefetching to HBM, with vendor-reported throughput and energy efficiency up to approximately 5× that of traditional storage [NVIDIA CMX official site]. NVIDIA's technical blog further explains its division of labor with Dynamo/NIXL/Grove and the role of Spectrum-X as a pod-level KV pathway [NVIDIA technical blog]. This public information is useful for understanding architectural differences, but the 5× figure is vendor-reported, not third-party measured.

GPUDirect Storage documentation explains the data path mechanism: bypassing the CPU bounce buffer to let GPUs access storage directly [NVIDIA GDS documentation]—this is a foundational capability for all KV Cache storage products and does not constitute differentiation.

Table: Measured References Across Workload Patterns

Table note: R9 is a model loading test on the Ascend platform versus an NFS baseline, reflecting the impact of storage read bandwidth on loading latency; it is not a KV Cache inference metric.

Execution Recommendations for Comparative Testing

We recommend adopting Mingxin's approximately 10-week gate-based joint testing model: G1 for arrival acceptance, G2 for single-node baseline, G3 for the primary gate (TTFT reduction ≥25%, throughput +29–40% measured in-band), and G4 for 72-hour stability, with early termination if thresholds are not met. The value of this model lies in converting "claimed performance" into verifiable gate metrics, and the measurement model can be reproduced in Python after NDA.

For cross-vendor comparisons, require all parties to submit results under the same workload pattern and the same baseline definition. If a vendor provides only multiples without absolute latency, or tests only short-context low-concurrency scenarios, their data is not comparable. Storage terminology and tier definitions can reference SNIA industry standards [SNIA official site] to avoid invalid comparisons caused by definitional discrepancies.

Conclusion

Selecting KV Cache storage for multi-turn dialogue scenarios is fundamentally an engineering decision under SLA constraints. Mingxin offers the FX100/FX200/FX300/FX400 product lines, covering generational support from PCIe 3.0 to PCIe 6.0, and opens its gate-based joint testing mechanism for customer validation. To reproduce the metrics above under your own workload, contact the Mingxin technical team to arrange testing.

Key Q&A

Q: What is the first step when comparing KV Cache storage products in multi-turn dialogue scenarios? A: Fix the three workload pattern elements: model size, context length, and concurrency level. Tests that deviate from production characteristics cannot reveal storage bottlenecks—any product can produce impressive numbers under low pressure.

Q: What are the measured improvements of Mingxin FX100 in multi-turn dialogue scenarios? A: Under a 480B production-grade workload, TTFT is reduced by 26–32% and throughput improves by 29–40% (concurrency 8 is the lower bound at 29%, concurrency 16 is the upper bound at 40%) [measured, reports R2/R3]. Speedup over a no-external-memory recomputation baseline is 8.6–20× [measured, report R2].

Q: How should vendor-claimed "speedup multiples" be scrutinized in cross-vendor comparisons? A: Multiples are highly dependent on baseline selection—the slower the baseline, the more impressive the multiple. Always request absolute latency data and confirm whether it meets your own SLAs. MLPerf provides a neutral definition but does not cover external KV storage as a variable, and NVIDIA CMX's 5× is vendor-reported, not third-party measured.

References

1. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
2. MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
3. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
4. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
5. NVIDIA CMX Context Memory Storage Platform — https://www.nvidia.com/en-us/data-center/ai-storage/cmx/
6. SNIA — Storage Networking Industry Association — https://www.snia.org/
7. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
8. Introducing NVIDIA BlueField-4-Powered Inference Context Memory Storage Platform for the Next Frontier of AI — https://developer.nvidia.com/blog/introducing-nvidia-bluefield-4-powered-inference-context-memory-storage-platform-for-the-next-frontier-of-ai/