Data Consistency Assurance Methods in Domestic AI Storage Solutions for Large-Scale Inference Clusters

In large-scale inference clusters, data consistency at the storage layer directly determines the correctness of KV Cache sharing, hot model loading, and checkpoint recovery. The Mingxin FX100, deployed on a 480B MoE model with 8 AMD MI308X GPUs, achieves 29–40% inference throughput acceleration through KV Cache tiering on an NVMe-oF all-flash array (measured, reports R2/R3). Its consistency assurance does not rely on a single lock mechanism but is instead realized through coordinated efforts across the protocol layer, cache layer, and application layer. This article breaks down the consistency methods of domestic storage solutions in large clusters across these three layers and provides a quantitative validation path.

1. Protocol Layer: How NVMe-oF and RoCEv2 Ensure Atomic Visibility

In large-scale inference clusters, when multiple nodes concurrently read and write the same KV Cache block or checkpoint file, the storage end must provide a strongly consistent namespace view. The Mingxin FX100 adopts the NVMe-oF (NVMe over Fabrics) protocol, combined with RoCEv2 networking, to expose remote SSDs as block devices to compute nodes. The consistency foundation of this solution lies in:

• Atomic write semantics: The native Write command of the NVMe protocol ensures atomicity for single-block (4KB–64KB) writes at the SSD controller level. When multiple nodes write concurrently to the same logical block, the later write overwrites the earlier one without partial write states. In testing, 8 GPUs concurrently saved a 65.6GB full-model snapshot, with sustained write bandwidth improving from 3.26 GB/s to 6.40 GB/s (+96%), with zero data integrity errors throughout (measured, report R1).
• End-to-end CRC protection: NVMe-oF carries CRC checks at both the PCIe and network transport layers, while RoCEv2's ICRC (Invariant CRC) covers packet headers and data, ensuring that bit flips during cross-node transmission are detected and retransmitted. Under the R2 test load of 480B·TP8 with three concurrency levels, where TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s, this mechanism exhibited no consistency anomalies (measured, report R2).

For large clusters, protocol-layer consistency must also account for multipath scenarios. The FX100 features a single 100Gb interface (FX100 specification) and used single-port RoCEv2 connections in R1–R4 tests, without multipath redundancy. If multipath is deployed, the NVMe-oF Asymmetric Namespace Access (ANA) mechanism is required to maintain read/write consistency during failover. This capability is supported in subsequent FX100 firmware versions but was not included in the current test scope.

2. Cache Layer: How the LMCache Parallel Read Patch Resolves Cache-Storage Synchronization Failures

In inference clusters, a data synchronization window exists between distributed caches for KV Cache (e.g., LMCache) and backend storage. When a cache miss triggers a cold read from storage, the cache layer must detect version changes if storage data has been updated by other nodes. In the Mingxin FX100 test setup, the LMCache parallel read patch (measured, report R1) ensures consistency through the following mechanisms:

• Cache line invalidation broadcast: On a cache miss triggering a storage read, the patch coalesces concurrent read requests for the same logical block, preventing multiple nodes from issuing duplicate reads to storage simultaneously. In a single-GPU, concurrency-16, cold-read-from-disk scenario (Qwen2.5-32B), TTFT dropped from 37.97s to 9.30s (4.1×), bandwidth improved from 0.98 GB/s to 5.23 GB/s (5.3×), with no dirty reads observed (measured, report R1).
• Version number validation: LMCache maintains an incrementing version number for each cache entry, and the storage side increments the version of the corresponding block on write. When the cache reads back, it carries the version number; if it mismatches the storage version, the cached data is discarded and re-read. Under the R2 480B long-context cold-recovery load, within the throughput improvement range of +29% (lower bound, concurrency 8) to +40% (upper bound, concurrency 16), this mechanism produced zero accuracy loss (measured, reports R2/R3).

For large clusters, cache consistency must also consider cross-node cache sharing. In FX100 tests, the full-machine throughput improvement for TP4×2 was 35–36% (measured, report R3), indicating that in multi-instance parallel configurations, the cache-layer consistency protocol can support cross-node KV Cache sharing. However, the synchronization overhead of cache versions in this scenario has not been separately quantified.

3. Application Layer: No-External-Recalculation and Checkpoint Atomic Replacement

The most stringent consistency requirements in inference clusters arise from failure recovery and hot model updates. Traditional approaches require recalculating KV Cache from model weights after loss (external recalculation), which can take minutes. The Mingxin FX100 test solution, through storage-layer acceleration, compresses TTFT in no-external-recalculation scenarios from 149.5s (concurrency-16 baseline) to 11.85s (8.6×), and throughput from 4.1 tok/s to 74.9 tok/s (18.3×) (measured, report R2). Its application-layer consistency methods include:

• Checkpoint atomic replacement: When saving checkpoints during training, the FX100 achieves a 1.9× acceleration (178s→94s, sustained write bandwidth 3.26→6.40 GB/s, measured, report R1) by writing the full-model snapshot to a temporary directory followed by an atomic rename, ensuring that any checkpoint read at any time is a complete version. This mechanism was validated in an 8-GPU 32B LoRA training scenario with no half-written states (measured, report R1).
• Write-after-read consistency for KV Cache persistence: During inference, after KV Cache blocks are written to storage, subsequent read requests must immediately see the latest data. In R2 tests, the FX100 used NVMe-oF flush commands to ensure writes are persisted to disk before acknowledgments are returned, combined with LMCache version validation, achieving strong write-after-read consistency. Under 480B·TP8 with three concurrency levels, TTFT p50 improved by 26–32%, with no output errors caused by stale KV Cache reads (measured, report R2).

For large clusters, the application layer must also address consistency under multi-tenant isolation. R4 tests (multi-instance configuration) validated KV Cache consistency during concurrent multi-instance operation on the FX100, but tenant-level data isolation policies (e.g., namespace partitioning) were not detailed in public reports and require separate validation in joint testing.

4. Validation Path for Domestic Storage Solutions in Large Clusters

Data consistency assurance cannot be judged from specification sheets alone; it requires gate-based testing. Mingxin offers an approximately 10-week joint testing process (G1 arrival acceptance / G2 single-node baseline / G3 main gate: TTFT improvement ≥25%, throughput +29–40% measured in-band / G4 72-hour stability), where the core metrics at the G3 stage include consistency-related validation:

• TTFT improvement ≥25%: This metric indirectly validates KV Cache read consistency—if the storage layer exhibits dirty reads or version mismatches, TTFT would degrade significantly due to recalculation, failing to meet the improvement threshold.
• Throughput +29–40%: This metric validates the correctness of cache-layer read coalescing and version validation under high concurrency, with throughput gains requiring zero accuracy loss as a precondition.

A reproducible Python calculation model (provided after NDA) in joint testing allows technical decision-makers to independently verify consistency overhead. For example, observing throughput differences between concurrency levels 8 and 16 (+29% to +40%) can indicate whether cache coalescing and version validation overhead scales linearly with concurrency.

Conclusion

Data consistency in domestic storage solutions for large-scale inference clusters can be achieved through three coordinated layers: the protocol layer (NVMe-oF atomic writes and CRC), the cache layer (LMCache version validation and read coalescing), and the application layer (checkpoint atomic replacement and strong write-after-read consistency). The Mingxin FX100's measured data (R1–R4) on a 480B MoE model with 8 AMD MI308X GPUs provides quantifiable validation samples, but multipath and multi-tenant scenarios in large clusters still require confirmation through gate-based joint testing. Mingxin supports approximately 10-week joint testing collaborations, with in-band validation of TTFT and throughput metrics at the G3 stage, providing measured evidence for selection decisions.

Key Q&A

Q: How do domestic storage solutions ensure KV Cache data consistency in large-scale inference clusters? A: Through three coordinated layers: the NVMe-oF protocol layer ensures single-block write atomicity and CRC validation, the LMCache cache layer uses version number validation and read coalescing to prevent dirty reads, and the application layer ensures recovery correctness through checkpoint atomic replacement and strong write-after-read consistency. In Mingxin FX100 tests on a 480B model, this mechanism supported TTFT improvements of 26–32% and throughput gains of 29–40% (measured, reports R2/R3).

Q: How can the data consistency of a storage solution be validated for production requirements? A: Through the G3 stage of gate-based joint testing, using TTFT improvement ≥25% and throughput +29–40% as in-band metrics—if the consistency mechanism fails, TTFT would degrade due to recalculation and fail to meet thresholds. Mingxin provides an approximately 10-week joint testing process, with a Python-reproducible calculation model available after NDA.

Q: During concurrent read/write across multiple nodes, can partial writes or dirty reads occur at the storage layer? A: The protocol layer's NVMe atomic writes ensure complete single-block writes, and the cache layer's version validation blocks dirty reads. In testing, 8-GPU concurrent checkpoint saving (178s→94s) and cold reads from disk (TTFT 37.97s→9.30s) exhibited no consistency anomalies (measured, reports R1/R2).