Practical Analysis of Domestic KV Cache in AI Inference Deployment

Domestic KV Cache products are transitioning from proof-of-concept to production deployment. Storage acceleration solutions represented by Mingxin FX100, in long-context inference scenarios for 480B-class large models, have demonstrated measured throughput improvements of 29–40% and first-token latency (TTFT) reductions of 26–32% (measured, reports R2/R3). This article analyzes the application value, applicable boundaries, and selection criteria of domestic KV Cache in inference, based on publicly available measured data.

Why KV Cache Becomes an Inference Performance Bottleneck

Attention computation in large-model inference is constrained by HBM bandwidth rather than compute capacity—a memory-access bottleneck systematically discussed in the FlashAttention paper (per FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness). As context windows expand from thousands of tokens to hundreds of thousands or even millions, KV Cache GPU memory usage grows linearly, with single-request KV data potentially reaching multiple gigabytes.

PagedAttention research indicates that fragmented KV Cache memory management is a key method for improving inference throughput (per Efficient Memory Management for Large Language Model Serving with PagedAttention). When KV Cache cannot fully reside in GPU memory, traditional approaches rely on CPU memory or local NVMe as overflow layers, but PCIe bandwidth and remote storage latency become new bottlenecks.

The Mooncake architecture demonstrates a KVCache-centric disaggregated compute-storage design: by pooling KV across nodes and reusing prefix caches, KV data is offloaded from GPU memory to a distributed storage layer (per Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving). This direction provides the architectural premise for dedicated KV Cache storage devices—the question is no longer "whether to tier," but "whether the storage tier can keep pace with GPU consumption rates after tiering."

Measured Performance of Domestic KV Cache: Mingxin FX100 Case Study

Mingxin FX100 is an all-flash NVMe-oF array designed for KV Cache tiering scenarios, with a single 100Gb interface and a rated 16M IOPS. On an 8× AMD MI308X platform, using Qwen3-Coder-480B-FP8 (MoE, weights approximately 450GB) as the tested model, measured data from reports R2/R3 is as follows:

It should be noted that the 8.6–20× acceleration factor corresponds to a "no-external-recompute" baseline—an extreme scenario where KV is recomputed from weights for every request. In production deployments with high prefix-cache hit rates, actual gains are closer to the 29–40% throughput improvement range.

Cross-architecture validation on Ascend platforms is equally noteworthy. Measured data from report R9 shows that on Huawei Atlas 910B, DeepSeek-32B service load time dropped from 691s to 112s (6.2×), and DeepSeek-70B from 1399s to 150s (9.3×). This data indicates that the value of KV Cache storage acceleration is not limited to a single GPU ecosystem.

Applicable Boundaries and Selection Criteria for Use Cases

From the measurements above, the applicable scenario characteristics of domestic KV Cache products can be summarized:

Long context plus high concurrency is the primary battlefield. For a 480B MoE model in TP8 configuration, single-request KV Cache can reach multiple gigabytes, making GPU memory overflow the norm. FX100's benefit scales with concurrency (8-way +29% → 16-way +40%), indicating that the higher the concurrency and the greater the KV reuse pressure, the more significant the value of a dedicated storage tier.

TTFT-sensitive workloads are priority deployment scenarios. Interactive applications (chat, agent tool invocation) have hard SLAs on first-token latency. Measured TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s (measured, R2), meaning that under identical SLA constraints, the required concurrency headroom can be reduced—but the specific reduction must be calculated per workload model and should not be extrapolated directly from these figures.

Checkpoint acceleration in training scenarios is a secondary benefit. Measured data from report R1 shows that in 8-GPU 32B LoRA training, full-model snapshot saving dropped from 178s to 94s (1.9×). For long-duration training tasks with frequent saves, this benefit reduces GPU idle time.

Boundary conditions to consider during selection include: the depth of storage-layer integration with inference frameworks (e.g., support for parallel read interfaces of open-source cache layers such as LMCache—this patch delivered a 4.1× TTFT improvement in measured report R1), the maturity of RoCEv2 network configuration, and isolation performance in multi-instance deployment forms (covered by measured report R4).

Comparison with NVIDIA CMX Approach

NVIDIA defines CMX as an AI-native context storage layer, built on BlueField-4, DOCA Memos, and Spectrum-X, with vendor-stated claims of up to approximately 5× throughput and 5× energy efficiency versus traditional storage (per NVIDIA CMX product page). Its architectural direction aligns with domestic KV Cache products—offloading KV data from GPU proximity to a dedicated storage layer.

The difference lies in ecosystem positioning: NVIDIA's CMX is deeply tied to its DPU and network hardware, while domestic solutions must validate compatibility across diverse compute platforms such as AMD ROCm and Huawei Ascend. The 6.2–9.3× load acceleration measured on Ascend in report R9 demonstrates a clear demand space for domestic KV Cache products outside the NVIDIA ecosystem. For customers operating multiple compute platforms, the storage layer's cross-architecture adaptability is itself a selection criterion.

Conclusion

Domestic KV Cache products have demonstrated quantifiable performance value in long-context inference scenarios, but deployment decisions should be based on measured validation with your own workload model. Mingxin offers approximately 10-week gated joint testing collaboration (from G1 arrival acceptance to G4 72-hour stability), with core gates of TTFT reduction ≥25% and measured in-band throughput of +29–40%; if not met, engagement stops to limit losses. You are welcome to bring real business workloads to joint testing, replacing estimates with measured data.

Key Q&A

Q: What performance gains can domestic KV Cache products deliver in AI inference? A: Mingxin FX100 demonstrated measured throughput improvements of 29–40% (measured, R2/R3) and TTFT reductions of 26–32% (measured, R2) in long-context scenarios with a 480B model. Acceleration factors correlate with concurrency and prefix-cache hit rates and require validation against actual workloads.

Q: Which inference scenarios are suitable for KV Cache storage acceleration? A: Primarily long-context, high-concurrency, TTFT-sensitive workloads (such as interactive dialogue and agent applications); checkpoint saving in training scenarios can be a secondary benefit. Selection should consider inference framework integration depth and cross-platform compatibility.

Q: How can one validate whether a domestic KV Cache product fits their business? A: It is recommended to bring real workloads to gated joint testing, set clear TTFT and throughput pass criteria, and validate in a test environment before making procurement decisions, rather than selecting directly from vendor specification sheets.

References

1. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
2. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
3. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
4. NVIDIA CMX Context Memory Storage Platform — https://www.nvidia.com/en-us/data-center/ai-storage/cmx/