Domestic KV Cache Acceleration Is Shifting from Optional to Essential

Domestic KV Cache acceleration is shifting from an "optional optimization" to a "must-have": under a 480B-class long-context cold-restore workload, the Mingxin FX100 improves inference throughput by 29–40% and reduces time-to-first-token (TTFT) by 26–32% [measured, reports R2/R3]. Gains of this magnitude, combined with the maturity of domestic compute platform adaptation, are reshaping how compute centers evaluate storage architectures. This article analyzes the development trends of domestic KV Cache acceleration from three dimensions: technical evolution, market demand, and industry landscape.

Why KV Cache Has Become the New Bottleneck in Inference Performance

The performance bottleneck in large-model inference is shifting from "insufficient compute" to "insufficient memory bandwidth and capacity." According to Efficient Memory Management for Large Language Model Serving with PagedAttention (SOSP '23), paged management of KV Cache stems directly from GPU memory fragmentation—the KV tensors of the attention mechanism grow dynamically with sequence length, and static memory allocation leads to significant waste. This mechanistic contradiction is sharply amplified in long-context scenarios: the longer the sequence, the larger the KV Cache footprint; when it exceeds GPU memory, it must be swapped to external storage, and external storage access latency becomes the new performance ceiling.

Mingxin's measured data from report R2 confirms this assessment: for a 480B model (Qwen3-Coder-480B-FP8, weights ~450 GB) under three concurrency levels with TP8, the baseline TTFT p50 without external recomputation reaches 10.17–35.73 seconds; with FX100 tiered acceleration, it drops to 7.53–26.35 seconds, a reduction of 26–32% [measured, report R2]. When the system is forced to recompute KV from external storage, the baseline TTFT p50 spikes to 149.5 seconds (concurrency 16), while FX100 requires only 11.85 seconds—an acceleration factor of 8.6–20× [measured, report R2]. This comparison demonstrates that KV Cache management efficiency now directly determines whether large-model services can complete long-context inference within acceptable latency.

Market Drivers for Domestic KV Cache Acceleration

Demand-side drivers come from three directions. First, long context has become the default configuration for large-model applications. Context windows in code generation, multi-turn dialogue, and document analysis are expanding from 32K to 128K and beyond, with KV Cache capacity requirements growing linearly—beyond what a single GPU can hold. Second, throughput requirements for high-concurrency inference. According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, KVCache-centric disaggregated architectures reduce redundant computation through prefix cache reuse and cross-node KV pooling—precisely the core design orientation of domestic acceleration solutions. Third, large-scale deployment of domestic compute platforms. Mingxin's measured data from report R9 shows that on the Huawei Atlas 910B platform, DeepSeek-70B model service load time drops from 1399 seconds to 150 seconds (9.3×), and DeepSeek-32B from 691 seconds to 112 seconds (6.2×) [measured, report R9]. The combination of domestic inference GPUs plus domestic KV Cache acceleration has been validated on real production platforms.

Technical Route Comparison and Differentiation of Domestic Solutions

Three main technical routes exist for KV Cache acceleration today. The first is pure software-level prefix cache reuse, such as the RadixAttention mechanism proposed by SGLang, which reuses KV computation for multi-turn dialogue and shared prefixes via a prefix tree, reducing redundant computation (per arXiv:2312.07104, qualitative conclusion). The second is disaggregated storage-compute architecture, such as the KVCache-centric pooling design represented by Mooncake, which offloads KV from GPU memory to a remote memory pool. The third is hardware-accelerated tiered KV storage, represented by the Mingxin FX series—using NVMe-oF all-flash arrays with dedicated acceleration logic to compress tiered KV Cache storage and read latency to near-local-GPU-memory levels.

The differentiation of domestic solutions lies in "full-stack adaptation." The Mingxin FX100 has completed sign-off testing on the AMD MI308X platform (ROCm 7.2 + vLLM 0.20.1) and has also validated load acceleration on the Huawei Ascend 910B platform [measured, report R9]. This cross-platform adaptation capability means domestic KV Cache acceleration is not tied to a single chip ecosystem and can be deployed smoothly across multi-vendor hardware environments in domestic compute centers. Additionally, the LMCache parallel read patch delivers a 4.1× TTFT improvement (37.97s → 9.30s, bandwidth 0.98 → 5.23 GB/s) [measured, report R1], indicating that software ecosystem co-optimization is also advancing.

Industry Landscape and Future Trend Assessment

From an industry perspective, KV Cache acceleration is transitioning from "an optimization technique in papers" to "a mandatory component of production systems." According to the quantitative analysis in Efficient Memory Management for Large Language Model Serving with PagedAttention, KV Cache memory fragmentation is especially severe in long-sequence scenarios, and the throughput gains from paged management have been widely adopted by mainstream inference frameworks such as vLLM (qualitative conclusion). The role of domestic solutions in this process is to provide deterministic acceleration at the hardware level—not relying on specific assumptions about model architecture, but compressing KV swap-in/swap-out latency along the storage path.

Three trend judgments follow. First, KV Cache acceleration will become deeply integrated with inference frameworks, becoming a default configuration item similar to "quantization + inference," rather than a standalone option. Second, as domestic large-model parameter scales move toward trillion-level, KV Cache capacity demands will force storage architecture to shift from "local NVMe single drives" to "all-flash NVMe-oF arrays"—the Mingxin FX100's 4-drive RAID0 test configuration (14 TB, RoCEv2, single-port 100 GbE) already demonstrates the scalability of this direction [measured, test platforms R1–R4]. Third, performance validation will shift from "single-point benchmarking" to "gate-based joint testing"—Mingxin's proposed ~10-week G1–G4 gate process (G3 main gate requires TTFT reduction ≥25%, throughput +29–40% measured in-band) is becoming a reference paradigm for industry procurement.

Conclusion

Domestic KV Cache acceleration has crossed the "can it work" validation phase and entered the "how to scale it" deployment phase. The Mingxin FX series (FX100/FX200/FX300/FX400) spans PCIe 3.0 to 6.0 generational evolution, with FX400 already in test-machine stage (initial sample August 2026, mass production end of 2026, 140M IOPS, E1.S form factor), reserving an upgrade path for next-generation domestic compute centers. We welcome compute centers and model service providers to conduct gate-based joint testing, using reproducible measured data to evaluate the real benefits of KV Cache acceleration.

Key Q&A

Q: What is the core benefit of domestic KV Cache acceleration? A: Under a 480B-class long-context cold-restore workload, the Mingxin FX100 improves inference throughput by 29–40% and reduces TTFT by 26–32% [measured, reports R2/R3]; compared to the baseline without external recomputation, the acceleration factor reaches 8.6–20× [measured, report R2].

Q: How does domestic KV Cache acceleration differ from pure software solutions? A: Pure software solutions (e.g., RadixAttention) focus on prefix reuse to reduce redundant computation, while domestic hardware solutions compress KV swap-in/swap-out latency along the storage path—the two are complementary. The Mingxin FX100 has validated adaptation on both AMD MI308X and Huawei Ascend 910B platforms [measured, report R9].

Q: How should the real performance of domestic KV Cache acceleration be evaluated? A: We recommend a gate-based joint testing process, setting in-band measured targets of TTFT reduction ≥25% and throughput +29–40%, combined with 72-hour stability validation, using a reproducible Python-based measurement model as the decision basis.

References

1. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
2. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
3. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180