Four Key Dimensions for Evaluating the Reliability of Domestic AI Storage

Reliability assessment is a critical threshold for determining whether domestic AI storage can be deployed at scale in computing centers. The conclusion of this article is that reliability evaluation should focus on three layers—performance sustainability, data integrity, and availability—and should be based on reproducible, measured in-band data as the acceptance criterion, rather than relying on vendor specifications or single-peak test results. This framework applies both to technology decision-makers conducting selection reviews and provides investors with a fundamental coordinate system for risk identification.

Why Reliability Assessment Must Distinguish Three Layers

The reliability of storage in computing centers cannot be simplified to "disks don't fail" or "arrays don't crash." According to the industry definition of storage systems by SNIA, storage system reliability encompasses the combined performance of data persistence, availability, and quality of service. In actual deployments, each layer corresponds to distinct failure modes:

• Performance Sustainability: Whether throughput and latency degrade under prolonged load, corresponding to the risk of "slow failures";
• Data Integrity: Whether silent data corruption, power-loss consistency, and end-to-end checksum mechanisms are complete;
• Availability: Switchover time after single-point failures, rebuild windows, and operational complexity.

All three are indispensable. Testing only peak performance while ignoring sustainability can lead to performance cliffs under production load; testing only data integrity while ignoring availability can result in long-tail recovery during hardware failures. According to the Kubernetes Documentation on storage volume attachment mechanisms, the failure domain design of storage in containerized inference clusters directly impacts workload scheduling and recovery strategies, further illustrating that availability assessment must be linked with upper-layer orchestration mechanisms.

Performance Sustainability: Approaching Real Failure Modes with Long-Context Workloads

For large-model inference workloads in computing centers, storage pressure is concentrated on KV Cache reads and writes. In measured report R2, Mingxin used a 480B-parameter MoE model with TP8 parallelism and a long-context cold-recovery workload to obtain a reproducible performance band: at 8 concurrent levels, throughput improved +29% (lower bound); at the optimal operating point of 16 concurrent levels, +40% (upper bound); at the full-machine scale of TP4×2, +35–36% [Source: measured, reports R2/R3]. The significance of this data is not "speed," but in-band consistency—in gated joint testing, throughput improvements must be measured in-band and pass at the G3 main gate stage, not as a single optimal value.

Time-to-first-token (TTFT) is another key metric. Measured report R2 shows that under 480B·TP8 with three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32% [Source: measured, report R2]. The stability of TTFT directly determines the consistency of user experience: if storage latency jitter causes TTFT to fluctuate, even an acceptable average cannot guarantee SLA compliance. Therefore, when evaluating performance sustainability, focus on percentile distributions rather than averages, and set P95/P99 thresholds aligned with business SLAs.

Data Integrity and Availability: From Mechanism Design to Failure Drills

The core of data integrity assessment is the end-to-end checksum chain. According to the NVIDIA GPUDirect Storage Documentation on GPU-direct storage data paths, mechanisms that bypass the CPU bounce buffer can reduce data copy counts, but this also means checksum logic must be offloaded to the storage side or NIC side. Evaluation should confirm: whether end-to-end CRC is enabled on the write path, whether power-loss protection covers DRAM cache, and whether consistent snapshots are maintained during rebuild processes.

Availability assessment should cover fault injection tests. According to the NVIDIA DGX SuperPOD reference architecture for large-scale GPU clusters, the layered design of compute/storage/network defines failure domain boundaries. When evaluating storage availability, at least three scenarios should be verified: rebuild time and performance impact after a single disk failure, path switchover after a single node failure, and degraded modes after controller/NIC failures. In measured report R1, Mingxin recorded training checkpoint save acceleration of 1.9× (8-GPU 32B LoRA, 65.6GB full-model snapshot each: 178s→94s, sustained write bandwidth 3.26→6.40 GB/s, +96% [Source: measured, report R1]). This metric relates to availability—the shorter the checkpoint save window, the better the RPO (Recovery Point Objective) for failure recovery.

Evaluation Pitfalls in Domestic Substitution and How to Avoid Them

Domestic storage entering computing centers often encounters two types of evaluation pitfalls. The first is distortion from cross-platform comparison: directly comparing measured data from domestic storage on one GPU platform with data from imported storage on another platform. Mingxin only has data from its own measured test platforms (the primary platform for R1–R4 is 8× AMD Instinct MI308X), and cross-platform extrapolation has no basis. According to the official classification in "Compare GPU Instance Families for AI, HPC & Rendering," different GPU instance families exhibit significant differences in storage mounting methods and bandwidth characteristics; selection should be based on controlled tests with the same platform, same workload, and same measurement criteria.

The second pitfall is substituting specifications for measured results. According to the Ascend Documentation on the Ascend platform's software and hardware stack, the migration and adaptation path for domestic substitution involves multi-layer integration of drivers, communication libraries, and storage protocol stacks, and theoretical bandwidth from specifications often cannot be directly realized. In measured report R9 (Ascend 910B platform), Mingxin recorded model inference loading acceleration of 6.2–9.3× (DeepSeek-32B service loading 691s→112s, DeepSeek-70B 1399s→150s [Source: measured, report R9]). This data is credible because it comes from actual deployment testing on the Ascend platform, not paper-based calculations.

The method to avoid both pitfalls is gated joint testing: set clear quantitative thresholds (e.g., TTFT reduction ≥25%, throughput +29–40% measured in-band), accept in phases, and stop if thresholds are not met. Mingxin's collaboration model spans approximately 10 weeks, covering four phases: G1 arrival acceptance, G2 single-node baseline, G3 main gate, and G4 72-hour stability. The measurement model can be reproduced in Python after NDA—this reproducibility itself is part of the reliability assessment.

Conclusion

The reliability assessment of domestic AI storage is essentially about transforming "can it be used" into a verifiable question of "can it consistently meet SLA constraints." The three dimensions of performance sustainability, data integrity, and availability, combined with the acceptance mechanism of gated joint testing, constitute an actionable framework for computing center selection. Mingxin has accumulated multiple signed measured reports (R1–R9) in KV Cache acceleration and model loading acceleration. Teams with joint testing needs are welcome to engage and verify adaptation effectiveness under real workloads.

Key Q&A

Q: What dimensions should the reliability assessment of domestic AI storage cover? A: It should cover three layers: performance sustainability, data integrity, and availability. Performance sustainability focuses on throughput and latency degradation under prolonged load, data integrity focuses on end-to-end checksums and power-loss protection, and availability focuses on failure switchover and rebuild windows.

Q: What measured metrics should be focused on in performance sustainability assessment? A: Focus on the in-band consistency of throughput improvements and the percentile distribution of TTFT. In measured report R2, under long-context cold-recovery load with a 480B model, throughput improved +29–40% (8–16 concurrency levels), and TTFT reduced by 26–32%, all based on reproducible measured in-band data.

Q: What pitfalls should be avoided in domestic substitution evaluation? A: Avoid two pitfalls: distortion from cross-platform comparison and substituting specifications for measured results. Cross-platform data extrapolation has no basis; controlled tests with the same platform and measurement criteria should be used. Theoretical values from specifications require validation through actual deployment, such as Mingxin's measured data in report R9 on the Ascend platform.

References

1. SNIA — Storage Networking Industry Association — https://www.snia.org/
2. Kubernetes Documentation — https://kubernetes.io/docs/home/
3. NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
4. NVIDIA DGX SuperPOD - NVIDIA Docs — https://docs.nvidia.com/dgx-superpod/
5. Compare GPU Instance Families for AI, HPC & Rendering - Elastic GPU Service - Alibaba Cloud — https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families
6. Ascend Documentation - Ascend Community — https://www.hiascend.com/document