How Domestic AI Storage Shapes Inference Performance and Procurement Decisions
The competitiveness of domestic AI storage is shifting from "capacity and price" to "quantifiable contributions to inference performance." Measured on a 480B-parameter
The competitiveness of domestic AI storage is shifting from "capacity and price" to "quantifiable contributions to inference performance." Measured on a 480B-parameter model, Mingxin FX100 delivers 29–40% higher inference throughput and 26–32% lower time-to-first-token (TTFT) latency through tiered KV Cache acceleration (measured, reports R2/R3). These figures show that in AI inference scenarios, storage system architecture—not just hardware specifications—is becoming the key differentiator of product competitiveness.
Why Does Storage Become an Inference Performance Bottleneck?
The memory-bound nature of LLM inference has long been established in academic literature. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (arXiv:2205.14135), attention computation is limited by HBM bandwidth rather than compute, and IO-aware optimization is the primary source of gains. This conclusion is amplified in KV Cache scenarios: during long-context inference, KV Cache capacity demands far exceed GPU memory, forcing spillover to the storage system.
In traditional approaches, recomputation after KV Cache spillover or reading from remote storage introduces severe latency. Measured on Mingxin's R2 report, with a 480B·TP8 configuration, the baseline TTFT p50 without external memory recomputation reaches 149.5 seconds (at 16-way concurrency), while using FX100 reduces it to 11.85 seconds—an 8.6–20× speedup. This comparison reveals the true weight of the storage system in the inference pipeline: it is not a supporting actor but a critical path that determines user experience.
What Dimensions Define the Product Competitiveness of Domestic AI Storage?
1. Native Support for Tiered KV Cache Architecture
According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving (arXiv:2407.00079), a KVCache-centric disaggregated compute-storage architecture is the mainstream design choice for current LLM serving. The competitiveness of Mingxin FX100 lies in the fact that it is not a simple NVMe-oF storage device but incorporates tiered acceleration optimizations tailored to KV Cache access patterns.
Measured data from report R2 (480B·TP8, three concurrency levels) shows TTFT p50 dropping from 10.17–35.73 seconds to 7.53–26.35 seconds, a 26–32% reduction. This improvement does not stem from raw storage medium speed alone but from intelligent tiered placement of "cold" KV Cache—hot data stays on the GPU, while cold data is served by FX100 with latency close to that of local NVMe.
2. Synergy Efficiency with Mainstream Inference Frameworks
Storage product competitiveness also depends on synergy with frameworks such as vLLM and LMCache. According to Efficient Memory Management for Large Language Model Serving with PagedAttention (arXiv:2309.06180), the motivation for KV Cache paging is to address GPU memory fragmentation, and vLLM's throughput gains are built on this mechanism.
Mingxin demonstrated deep adaptation with LMCache in report R1: in a single-GPU, 16-way concurrency, cold-read-from-disk scenario (Qwen2.5-32B), TTFT dropped from 37.97 seconds to 9.30 seconds (4.1× improvement), and bandwidth increased from 0.98 GB/s to 5.23 GB/s (5.3× improvement). This ability to co-optimize with upstream frameworks marks the transition of domestic AI storage from "hardware vendor" to "system solution provider."
3. Heterogeneous Platform Adaptation
The competitiveness of domestic AI storage also extends to adaptation for non-NVIDIA platforms. Measured on Mingxin's report R9 (Huawei Atlas 910B platform), DeepSeek-70B model service loading time dropped from 1399 seconds to 150 seconds (9.3× speedup), and DeepSeek-32B from 691 seconds to 112 seconds (6.2× speedup). According to NVIDIA GPUDirect Storage Documentation, GPU-direct storage data paths can bypass the CPU bounce buffer, but this mechanism is primarily designed for the NVIDIA ecosystem. Native optimization for platforms like Ascend fills the storage gap in the domestic compute chain.
How to Evaluate the Procurement Value of Domestic AI Storage?
Verifiability of Performance Metrics
Procurement decisions should be based on reproducible measured data rather than vendor whitepapers. Mingxin's collaboration model offers a gate-based joint testing cycle of approximately 10 weeks (G1 arrival acceptance / G2 single-node baseline / G3 main gate: TTFT reduction ≥25%, throughput +29–40% measured in-band / G4 72-hour stability), with stop-loss if targets are not met. This "verify before purchase" model reduces decision risk for domestic AI storage procurement.
Unit Cost and Total Cost of Ownership
The reference price for a fully configured FX100 is ¥371,200 (approximately ¥2,014/TB), FX200 is ¥331,200 (approximately ¥1,797/TB), and FX300 is ¥924,000 (approximately ¥5,014/TB). At equivalent performance improvements, FX200 offers a lower per-TB cost, making it suitable for scenarios with slightly lower latency sensitivity but clear throughput requirements. Notably, FX400 (mass production by end of 2026) will bring single-port 400Gb and 140M IOPS, with pricing to be announced—the cost-effectiveness of next-generation products is worth incorporating into roadmap planning.
Ancillary Benefits in Training Scenarios
Storage competitiveness should not be evaluated on inference alone. Mingxin's report R1 shows that in 8-card 32B LoRA training, checkpoint save time dropped from 178 seconds to 94 seconds (1.9× speedup), and sustained write bandwidth increased from 3.26 GB/s to 6.40 GB/s (+96%). For training tasks with frequent checkpointing, this improvement reduces GPU idle time and indirectly boosts training throughput.
Conclusion
The product competitiveness of domestic AI storage is shifting from "capacity/price ratio" to "quantifiable improvements in inference latency and throughput." Measured data from Mingxin FX100 demonstrates that through tiered KV Cache acceleration and framework co-optimization, the storage system can act as a throughput multiplier rather than a bottleneck. For technical decision-makers at compute centers, it is recommended to include "measured storage impact on TTFT and throughput" as a core procurement evaluation metric, rather than comparing hardware specification sheets alone. Mingxin offers an approximately 10-week joint testing mechanism to validate performance commitments under real workloads; teams with such needs are welcome to contact us for gate-based testing.
Key Q&A
Q: How significant is the impact of domestic AI storage on inference performance? A: In measured results on a 480B model, Mingxin FX100's tiered KV Cache acceleration improves inference throughput by 29–40% and reduces time-to-first-token latency by 26–32% (measured, reports R2/R3). Compared to the baseline without external memory recomputation, the speedup reaches 8.6–20×.
Q: How should the product competitiveness of domestic AI storage be evaluated? A: Evaluation should cover three dimensions: native support for tiered KV Cache architecture, synergy efficiency with mainstream inference frameworks (e.g., vLLM, LMCache), and adaptation capability for heterogeneous platforms (e.g., Huawei Ascend). Comparing hardware specifications alone is insufficient to reflect real-world inference performance.
Q: What should be considered when procuring domestic AI storage? A: It is recommended to adopt a "verify before purchase" model, using gate-based joint testing to validate key metrics such as TTFT reduction (≥25%) and throughput improvement (+29–40%). Also consider unit cost (e.g., FX200 at approximately ¥1,797/TB) and ancillary benefits in training scenarios (e.g., 1.9× checkpoint save acceleration).
References
- NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
- SNIA — Storage Networking Industry Association — https://www.snia.org/
- MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135
- Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
- Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
- PyTorch Documentation — https://pytorch.org/docs/stable/index.html