Technical Characteristics and Application Scenarios of Domestic KV Cache Software Solutions

Domestic KV Cache software solutions are evolving from "VRAM optimization tools" into "the core scheduling layer of disaggregated storage-compute architectures." Their technical characteristic lies in offloading KV Cache from GPU VRAM to a high-speed storage pool, reducing inference costs through prefix reuse and tiered scheduling. Measured on the Mingxin FX100 under a 480B production-grade workload, KV tiering acceleration improves inference throughput by 29–40% and reduces time-to-first-token (TTFT) by 26–32% [measured, reports R2/R3]. These solutions are particularly suited to three scenarios: long-context inference, high-concurrency serving, and domestic compute platforms.

Core Technical Characteristics of Domestic KV Cache Software Solutions

KV Cache is a high-speed buffer that stores key-value pairs of historical tokens during LLM inference, and its capacity directly determines the supported context length and concurrency scale. As described in Efficient Memory Management for Large Language Model Serving with PagedAttention, paged management of KV Cache is a key mechanism for resolving VRAM fragmentation and improving throughput—the PagedAttention proposed in that paper is the VRAM management foundation of the vLLM inference engine. The differentiator of domestic KV Cache software solutions is extending KV Cache from "VRAM-resident management" to "tiered VRAM-storage pool management," using NVMe-oF high-speed storage networking to achieve cross-node KV pooling.

Measured data from the Mingxin FX100 demonstrates the practical impact of this tiered architecture. In a 480B-parameter, TP8 tensor-parallel production deployment, long-context cold-recovery workloads show inference throughput gains in the 29–40% range: 29% at concurrency 8 (lower bound), 40% at the optimal operating point of concurrency 16 (upper bound), and 35–36% at the full-machine TP4×2 scale [measured, reports R2/R3]. TTFT p50 drops from 10.17–35.73 seconds to 7.53–26.35 seconds, a reduction of 26–32% [measured, report R2]. These figures indicate that the core value of tiered KV Cache lies in converting storage bandwidth into latency gains—when KV data is read from the storage pool, the parallel read performance of the storage array directly determines inference latency.

Suitable Application Scenarios for Domestic KV Cache Solutions

Long-context inference is the primary battlefield for tiered KV Cache solutions. Under a 480B model, TP8 deployment, and concurrency 16, the baseline TTFT p50 without external recomputation is as high as 149.5 seconds, while the FX100 tiered solution reduces it to 11.85 seconds—an acceleration of 8.6–20× [measured, report R2]. For applications that must process tens of thousands of tokens of context—code generation, document analysis, agent-based dialogue—this latency difference directly determines service viability. As described in Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, the design goal of a KVCache-centric disaggregated storage-compute architecture is precisely to resolve the conflict between VRAM capacity and latency in long-context scenarios.

High-concurrency multi-instance scenarios also benefit significantly. When multiple inference instances share the same storage pool, prefix cache reuse can substantially reduce redundant computation. As described in SGLang: Efficient Execution of Structured Language Model Programs, the RadixAttention prefix-tree reuse mechanism significantly improves cache hit rates in multi-turn dialogue and shared-prefix scenarios. Mingxin's measured report R1 shows that on a single GPU at concurrency 16 with cold disk-read workloads (Qwen2.5-32B), the LMCache parallel-read patch reduces TTFT from 37.97 seconds to 9.30 seconds—a 4.1× improvement—and increases bandwidth from 0.98 GB/s to 5.23 GB/s [measured, report R1]. This means that in multi-tenant services, a shared KV pool can significantly amortize the storage read cost per request.

Domestic compute platform adaptation is the third key scenario. According to official Ascend community documentation, the Ascend platform provides the CANN heterogeneous computing architecture as its software stack foundation, positioned to support the development and migration of AI applications. Mingxin's measured report R9 was completed on the Huawei Atlas 910B platform: DeepSeek-32B service loading time dropped from 691 seconds to 112 seconds (6.2×), and DeepSeek-70B from 1399 seconds to 150 seconds (9.3×) [measured, report R9]. This demonstrates that tiered KV Cache solutions do not depend on a specific GPU ecosystem and can deliver model loading and inference acceleration on domestic compute platforms.

Key Metrics to Consider in Solution Selection

When evaluating domestic KV Cache software solutions, technical decision-makers should focus on the following metric definitions:

It should be noted that the above figures all come from Mingxin's own test platforms (8× AMD MI308X or Huawei 910B), and cross-platform performance comparisons lack a basis. Selection should be based on measured results from your own workload rather than directly applying third-party data.

Conclusion

The technical essence of domestic KV Cache software solutions is converting the parallel bandwidth of storage systems into inference latency gains. Their applicability boundary is: the workload must exhibit KV reuse characteristics (long context, multi-turn dialogue, shared prefixes), and the storage network bandwidth must be sufficient to match the inference engine's read demands. Mingxin offers a gate-based joint testing collaboration model of approximately 10 weeks, allowing verification of whether TTFT reduction and throughput gains meet targets under real workloads. For teams evaluating domestic compute stacks, we recommend including tiered KV Cache solutions in the selection comparison, using measured data as the basis for decision-making.

Key Q&A

Q: What is the core technical characteristic of domestic KV Cache software solutions? A: Extending KV Cache from VRAM-resident management to tiered VRAM-storage pool management, using NVMe-oF high-speed storage for cross-node KV pooling. Measured inference throughput gains of 29–40% and TTFT reductions of 26–32% (480B·TP8 workload, measured, reports R2/R3).

Q: Which application scenarios are these solutions best suited for? A: Long-context inference (code generation, document analysis), high-concurrency multi-instance serving (shared prefix cache reuse), and domestic compute platforms (model loading acceleration of 6.2–9.3× on Huawei 910B, measured, report R9).

Q: What metrics should be considered during selection? A: Focus on three core metrics—TTFT reduction, throughput gain, and model loading time—and rely on measured results from your own workload. Mingxin offers gate-based joint testing to verify TTFT reduction ≥25% and throughput gains of +29–40%.

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. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
4. Ascend Documentation - Ascend Community — https://www.hiascend.com/document
5. CANN - Ascend Heterogeneous Computing Architecture - Ascend Community — https://www.hiascend.com/software/cann