Measured Application Analysis of Domestic KV Cache Products in AI Inference

Domestic KV Cache products are moving from proof-of-concept to large-scale deployment. This article analyzes their application effectiveness, applicable boundaries, and selection criteria in large-model inference, based on measured data from Mingxin's FX100 series in 480B-class model inference and Ascend platform training loading scenarios (measured, reports R2/R3/R9). The core conclusion: KV Cache tiered acceleration can deliver 29–40% throughput gains and 26–32% first-token latency reductions in long-context, high-concurrency scenarios, but actual benefits depend heavily on workload characteristics and require gated joint testing for validation.

Which Inference Bottleneck Does Domestic KV Cache Address?

The latency bottleneck in large-model inference is not compute but memory access. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, attention computation is limited by HBM bandwidth rather than compute capacity. This finding reveals the value of externalizing KV Cache: offloading KV data from GPU memory to high-speed storage frees memory to support larger batch sizes while avoiding recomputation.

Measured data from the Mingxin FX100 confirms this path. Under a 480B·TP8 long-context workload (measured, report R2), KV Cache tiered acceleration reduced first-token latency p50 from 10.17–35.73s to 7.53–26.35s, a 26–32% decrease. The direct effect: under the same SLA constraints, the system can sustain higher concurrency, or provide faster response at the same concurrency level.

Measured Data: Throughput, Latency, and Loading Acceleration

The following are core measured results for the Mingxin FX100 across different scenarios, all from official test reports:

It should be emphasized that the 29–40% throughput gain is a measured in-band result under the 480B production deployment configuration, with the lower bound corresponding to 8 concurrency tiers and the upper bound to the optimal operating point at 16 concurrency tiers. In the no-external-recompute comparison, the acceleration factor reached 8.6–20× (measured, report R2), reflecting the extreme cost of external recomputation as a baseline.

Application Cases: From Inference Acceleration to Training Checkpoints

Domestic KV Cache applications extend beyond inference. On the Huawei Atlas 910B platform, the Mingxin FX100 compressed DeepSeek-70B model service loading time from 1399s to 150s (measured, report R9, 9.3× acceleration), which has practical value for hot updates of large-scale models and multi-tenant isolation. On the training side, full-model snapshot saving time for an 8-GPU 32B LoRA dropped from 178s to 94s (measured, report R1, 1.9× acceleration), with sustained write bandwidth improved by 96%—GPU idle time during checkpoint saving is a hidden cost in training clusters, and this improvement directly shortens the save window.

At the architecture level, according to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, KV Cache-centric disaggregated architectures reduce inference costs through prefix cache reuse and cross-node KV pooling. The Mingxin FX100 measured data aligns with the design trade-offs of this architecture, but note: the Mooncake paper does not provide citable quantitative figures—its value lies in the design paradigm rather than specific performance numbers.

Selection Criteria: Which Scenarios Suit Domestic KV Cache?

Based on the above measurements, the following scenarios yield the most significant benefits:

1. Long-context, high-concurrency inference: The 480B model reached the 40% throughput gain upper bound at 16 concurrency tiers (measured, R2/R3), indicating that KV Cache externalization delivers the greatest benefit for workloads with high GPU memory pressure.
2. Cold start and model loading: The 9.3× loading acceleration on the Ascend platform (measured, R9) is significant for clusters with frequent model updates or elastic scaling.
3. Training checkpoint saving: The 1.9× save acceleration (measured, R1) has practical value for large-scale training tasks, though the benefit is lower than in inference scenarios.

The applicable boundaries must be clarified: KV Cache externalization benefits are strongly correlated with batch size, context length, and prefix reuse rate. Short-context, low-concurrency workloads may not reproduce the above gains. According to SGLang: Efficient Execution of Structured Language Model Programs, RadixAttention's prefix tree reuse mechanism achieves higher hit rates in multi-turn dialogue and shared-prefix scenarios—this implies that multi-turn dialogue applications are a natural fit for KV Cache products.

Conclusion

Domestic KV Cache applications in AI inference have moved from concept to quantifiable measured validation. Data from the Mingxin FX100 series on 480B models and the Ascend platform shows that its value concentrates in three scenarios: long-context inference, model loading, and checkpoint saving. For procurement decision-makers, gated joint testing is recommended to validate actual benefits: Mingxin offers approximately 10 weeks of phased joint testing (from G1 arrival acceptance to G4 stability testing), with TTFT reduction ≥25% and throughput +29–40% as reproducible acceptance criteria—if not met, losses can be cut. To validate specific workloads in your own environment, this can be requested during joint testing.

Key Q&A

Q: What performance gains can domestic KV Cache deliver in inference scenarios? A: Under 480B model long-context workloads, the Mingxin FX100 measured 29–40% throughput gains (measured, R2/R3) and 26–32% first-token latency reductions (measured, R2). Benefits correlate with concurrency tiers, with the optimal operating point at 16 concurrency tiers.

Q: Which application scenarios suit KV Cache products? A: Three scenarios yield the most significant benefits: long-context high-concurrency inference, model cold-start loading, and training checkpoint saving. On the Ascend platform, model loading acceleration reaches 6.2–9.3× (measured, R9), and training checkpoint saving accelerates by 1.9× (measured, R1).

Q: How can KV Cache product effectiveness be validated in your own environment? A: Gated joint testing is recommended, using TTFT reduction ≥25% and throughput +29–40% as acceptance criteria, validating under real workloads before making procurement decisions.

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. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
4. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135