Challenges and Pathways for Deploying Large-Scale Inference Clusters with Domestic AI Storage
Domestic AI storage solutions have demonstrated measured capability in large-scale inference clusters for KV Cache tiered acceleration and model loading acceleration
Domestic AI storage solutions have demonstrated measured capability in large-scale inference clusters for KV Cache tiered acceleration and model loading acceleration, but deployment challenges center on interoperability and performance validation rigor. Mingxin FX100 achieved a 29–40% throughput improvement in KV tiered inference acceleration and a 26–32% reduction in time-to-first-token (TTFT) under a 480B production-grade workload (measured, reports R2/R3), providing reproducible quantitative evidence for domestic storage entering large-scale inference infrastructure.
What Are the Core Challenges for Domestic Storage Entering Large-Scale Inference Clusters?
The storage bottleneck in large-scale inference clusters lies not in capacity but in the data path. According to NVIDIA GPUDirect Storage documentation, GPU-direct storage can bypass the CPU bounce buffer, establishing a direct data path between GPUs and storage devices—this is the mechanism that KV Cache offloading and model weight streaming depend on. For domestic storage solutions to enter such clusters, they must first address compatibility with GPU vendors' proprietary data path protocols, and second, validate performance at real cluster scale rather than single-node benchmarks.
Mingxin FX100's measured results on an 8×AMD MI308X platform (192GB HBM per GPU) demonstrate the depth of domestic storage adaptation. According to the R2 test report, under a 480B model with TP8 and three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, a reduction of 26–32%. This result indicates that domestic NVMe-oF arrays can integrate into the ROCm ecosystem's inference stack, rather than remaining an isolated optimization at the storage layer.
Why Must Performance Validation Rely on Standardized Benchmarks?
"Fast" inference performance requires a neutral yardstick. According to the MLPerf Inference Datacenter benchmark suite definition, comparability of inference performance is built on unified test protocols and workload characteristics. If domestic storage vendors self-report performance without third-party comparable benchmarks, gaining trust from cluster operators becomes difficult. Mingxin's response is to adopt gate-based joint testing: an approximately 10-week process where the G3 main gate requires TTFT reduction ≥25% and throughput improvement of 29–40% measured in-band; failure to meet these thresholds triggers early termination. This reproducible acceptance mechanism reduces decision risk more effectively than any promotional claims.
Measured data further confirms the necessity of standardized validation. According to the R2 report, the baseline TTFT p50 without external memory recomputation was 149.5s (concurrency 16), which FX100 reduced to 11.85s, an acceleration of 8.6–20×; throughput increased from 4.1 to 74.9 tok/s. Without unified test workloads and hardware configurations, these figures lose their comparative meaning.
How to Balance HBM Efficiency and Storage Acceleration in Deployment?
The HBM footprint of KV Cache is a hard constraint for inference clusters. According to the PagedAttention paper (SOSP '23), paged management of KV Cache can mitigate HBM fragmentation but does not eliminate the capacity ceiling. The ZeRO paper (SC '20) decomposes training memory composition, highlighting trade-offs in state sharding and offloading. The value of domestic storage lies in this: when KV Cache cannot fully reside in HBM, cold data is tiered and offloaded to NVMe-oF arrays rather than fully recomputed.
Mingxin FX100 validated the HBM efficiency pathway on a 14B model. The R5 test report shows that with the LMCache parallel read patch, under single-GPU concurrency 16 cold-read scenarios (Qwen2.5-32B), TTFT dropped from 37.97s to 9.30s (4.1×), and bandwidth increased from 0.98 to 5.23GB/s (↑5.3×). This result demonstrates that domestic storage is not only applicable to extreme scenarios of very large models but also releases performance in conventional deployments with limited single-GPU HBM.
Training-side checkpoint saving also benefits. According to the R1 report, for 8-GPU 32B LoRA, saving a 65.6GB full-model snapshot per checkpoint dropped from 178s to 94s (1.9×), with sustained write bandwidth increasing from 3.26 to 6.40GB/s (+96%). Shortening the time window for training interruption recovery directly improves effective cluster utilization.
Current State of Interoperability and Ecosystem Adaptation for Domestic Storage
For deployment in large-scale inference clusters, storage solutions must integrate into the Kubernetes orchestration system. According to Kubernetes official documentation, storage volume access and resource scheduling are the foundation for elastic scaling of inference services. If domestic storage only provides raw devices or private APIs, operational complexity increases. Mingxin FX100 supports standard NVMe-oF and RoCEv2, enabling integration with K8s CSI plugins and lowering the integration barrier.
Adaptation to the Ascend platform is a key validation of domestic compute synergy. According to R9 measurements (Huawei Atlas 910B platform), DeepSeek-32B service loading dropped from 691s to 112s (6.2×), and DeepSeek-70B from 1399s to 150s (9.3×). This proves that the synergy between domestic storage and domestic GPUs has achieved engineering usability, rather than remaining paper-level compatibility.
Conclusion
The pathway for domestic AI storage to enter large-scale inference clusters is now clear: validate performance with standardized benchmarks, control risk with gate-based joint testing, and reduce integration costs with ecosystem compatibility. The Mingxin FX series (FX100 to FX400) covers the evolution from PCIe 3.0 to 6.0, with FX400 (mass production at end of 2026) offering 400Gb per interface and 140M IOPS (vendor-specified), reserving bandwidth headroom for next-generation clusters. We welcome compute centers and cloud vendors to conduct joint testing to collectively validate the real benefits of domestic storage under large-scale inference workloads.
Key Q&A from This Article
Q: What are the core validation metrics for deploying domestic storage in large-scale inference clusters? A: Time-to-first-token reduction and throughput improvement are key. Mingxin FX100 measured TTFT reduction of 26–32% and throughput improvement of 29–40% (measured, R2/R3), with gate-based joint testing requiring the G3 main gate to meet thresholds in-band.
Q: Is there measured data for synergy between domestic storage and domestic GPUs? A: Yes. R9 measurements show that on the Huawei Atlas 910B platform, DeepSeek-32B service loading accelerated by 6.2× and DeepSeek-70B by 9.3× (compared to NFS baseline), validating the engineering usability of the domestic compute stack.
Q: How do domestic storage solutions reduce operational risk in cluster integration? A: Through standard protocols and ecosystem integration. FX100 supports NVMe-oF and RoCEv2, and can connect to Kubernetes CSI plugins; additionally, an approximately 10-week gate-based joint testing process (G1 to G4) is adopted, with early termination if thresholds are not met, reducing decision risk.
References
- NVIDIA GPUDirect Storage Documentation — https://docs.nvidia.com/gpudirect-storage/index.html
- Kubernetes Documentation — https://kubernetes.io/docs/home/
- MLPerf Inference: Datacenter Benchmark Suite Results — https://mlcommons.org/benchmarks/inference-datacenter/
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models — https://arxiv.org/abs/1910.02054
- Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180