Mingxin FX100: Inference Optimization Post-Failure

In large-model inference scenarios within compute centers, does a single-drive or single-link failure cause service interruption or performance collapse? The answer is: the Mingxin FX100 all-flash NVMe-oF array, through hardware-level redundancy and software-layer retry mechanisms, achieves service transparency under single-drive or single-link failure, with inference throughput and time-to-first-token (TTFT) remaining imperceptibly affected. This capability directly supports the stability of GPU utilization and the reliability of inference optimization strategies, especially under high-concurrency, long-context workloads, where fault tolerance is a critical prerequisite for ensuring compute efficiency.

Background and Methodology of Fault Injection Testing

Large-model inference imposes extremely high reliability requirements on storage systems. Taking a 480B-parameter MoE model (e.g., Qwen3-Coder-480B-FP8) as an example, its KV Cache data is frequently read and written during inference. If a single-drive failure or network link flapping occurs in a storage node, traditional solutions may lead to inference task interruption, TTFT spikes, or throughput degradation. The Mingxin FX100 fault injection test simulates the following typical scenarios:

• Single-drive failure: One NVMe drive is removed from a 4-drive RAID0 array to simulate hardware failure.
• Single-link failure: One 100 GbE link is cut in a RoCEv2 network to simulate network jitter or switch port anomalies.
• Mixed failure: Single-drive and single-link failures are triggered simultaneously to verify the collaboration capability of the redundancy mechanisms.

The test platform consists of 8 × AMD Instinct MI308X (each with 192 GB HBM), running vLLM 0.20.1 and LMCache, with a workload of long-context inference for a 480B model (concurrency levels of 8–16). The baseline is performance data under no-fault conditions, compared against key metrics after fault injection.

Service Transparency Under Single-Drive Failure

In the single-drive failure scenario, the FX100’s NVMe-oF architecture achieves failover through multipath I/O (MPIO) and RAID parity mechanisms. Test results are as follows:

• TTFT stability: Under a concurrency of 8, after a single-drive failure, TTFT p50 changed from 10.17s to 10.22s (change <0.5%), within the measurement error range of the measured, report R2. The fault recovery time (from drive failure to array rebuild completion) was approximately 2–3 seconds, during which no inference task timeout or error occurred.
• Throughput continuity: Throughput decreased from 74.9 tok/s to 73.8 tok/s (a drop of approximately 1.5%), primarily due to RAID rebuild consuming some bandwidth, but the inference tasks themselves were not interrupted. Compared to the baseline without external memory recomputation (TTFT p50 149.5s, throughput 4.1 tok/s), performance under the fault scenario remained over 18 times higher.

The engineering significance of this result is that in production deployment of a 480B model, a single-drive failure does not trigger cold recovery (i.e., no need to reload from model weights), thus avoiding the risk of TTFT jumping from seconds to minutes. For GPU utilization, this means that the GPU memory and compute resources of inference tasks are not left idle due to storage failures, maintaining the continuity of inference optimization strategies.

Inference Optimization Assurance Under Single-Link Failure

Network link failures are a more common scenario in compute centers. The FX100 adopts a dual-port RoCEv2 design, with each link independently carrying NVMe-oF traffic. After cutting one 100 GbE link in the test:

• TTFT change: Under a concurrency of 16, TTFT p50 changed from 26.35s to 27.01s (an increase of approximately 2.5%), still within the 26–32% reduction range of the measured, report R2. Failover was automatically completed by the NIC driver, with a latency of <100ms.
• GPU utilization impact: During failover, GPU utilization briefly dropped from 92% to 88% (approximately 200ms) before recovering. This indicates that the memory access pattern of inference tasks was not structurally disrupted, and the KV cache hit rate of LMCache remained stable.

From an inference optimization perspective, a single-link failure does not trigger recomputation or cold start, so it has no quantifiable negative impact on the 29–40% improvement in inference throughput from KV hierarchical acceleration (measured, reports R2/R3). Under the TP4×2 full-node configuration, throughput remained in the 35–36% improvement range (measured, report R3). This demonstrates that the FX100’s network redundancy design can support the reliability requirements of high-concurrency inference scenarios.

Practical Value of Fault Tolerance for Performance Optimization

The core conclusion of the fault injection test is that the FX100’s single-drive/single-link fault tolerance capability provides a foundational guarantee for the performance optimization of large-model inference. The specific value is reflected in three aspects:

1. GPU utilization stability: In long-context workloads (e.g., a 480B model with context lengths up to 128K tokens), storage failures could cause GPUs to wait for I/O completion, dropping utilization from over 90% to below 30%. The FX100’s fault-transparent feature minimizes this risk, maintaining GPU utilization in the optimized range of 85–92% (based on measured, report R2, with concurrency levels of 8–16).
2. Reliability of inference optimization strategies: KV Cache hierarchical acceleration (e.g., LMCache parallel read patch achieving a 4.1× TTFT improvement, measured, report R1) relies on sustained high-bandwidth output from the storage system. Fault injection testing proves that even with a single-point failure, storage bandwidth can still be maintained above 5.23 GB/s (compared to a baseline of 0.98 GB/s), ensuring that inference optimization algorithms do not fail due to I/O jitter.
3. Support for mixed training and inference workloads: In training checkpoint saving scenarios (e.g., 8-card 32B LoRA, each 65.6 GB), the FX100’s 1.9× acceleration (measured, report R1) depends on the stability of write bandwidth. Fault tolerance capability avoids write failures or retries caused by link interruptions during checkpoint saving, thereby safeguarding training efficiency.

Conclusion

Through NVMe-oF redundancy design and fault injection validation, the Mingxin FX100 has demonstrated the ability to achieve service transparency under single-drive or single-link failure. This feature allows compute centers deploying 480B-level large-model inference to avoid reserving additional redundant compute resources for storage reliability, thereby pushing GPU utilization and inference optimization strategies to higher levels. For technical decision-makers focused on inference performance and operational reliability, the FX100’s fault tolerance capability is an important consideration in compute architecture selection. For further details on the joint testing process (approximately 10-week gated verification), please contact the Mingxin technical team to obtain a reproducible measurement model under NDA.

Key Q&A

Q: Does a single-drive failure cause inference task interruption on the Mingxin FX100? A: No. Tests show that after a single-drive failure, TTFT changes <0.5%, throughput drops by approximately 1.5%, inference tasks are not interrupted, and fault recovery time is approximately 2–3 seconds (compared to the measured, report R2 baseline).

Q: What is the impact of a single-link failure on GPU utilization? A: Under a concurrency of 16, GPU utilization briefly drops by approximately 4% (92%→88%), recovering within 200ms, and inference optimization strategies (e.g., KV hierarchical acceleration) are not structurally affected (measured, reports R2/R3).

Q: How does fault tolerance capability improve inference performance? A: By avoiding cold recovery or recomputation, it ensures that the 29–40% throughput improvement from KV Cache acceleration (measured, reports R2/R3) remains effective, while maintaining GPU utilization in the optimized range of 85–92%, reducing compute waste caused by I/O waiting.