Compute Center 72-Hour Stress Test for Acceptance

In the acceptance process of compute center construction and expansion, peak performance metrics often attract the most attention—IOPS, bandwidth, first-token latency—but these numbers only reflect the system's capability at its "best moment." What truly determines production readiness is whether the system can maintain performance consistency under sustained high load. The 72-hour stability stress test is the final gate for compute center acceptance: it verifies whether the storage, network, and compute layers experience performance degradation, resource leaks, or fault accumulation during prolonged collaboration.

For inference optimization scenarios, KV cache acceleration solutions (e.g., NVMe-oF-based all-flash arrays) can easily demonstrate impressive speedup ratios in short tests. However, during extended operation, factors such as cache hit rate changes, garbage collection triggers, and network congestion may cause GPU utilization fluctuations. This article uses measured data from the Mingxin FX100 on the AMD MI308X platform as an example to explore how gate-based stability testing ensures the long-term effectiveness of inference optimization solutions.

Core of Stability Stress Testing: Performance Consistency, Not Peaks

Traditional storage acceptance often focuses on 4K random read IOPS or sequential write bandwidth, but KV cache acceleration scenarios demand stricter performance consistency. Inference service token generation is a serial dependency process: if the time to first token (TTFT) suddenly increases by 2–3 times at a certain point, downstream GPU compute units will experience idle waiting, directly reducing overall throughput.

In the measured report R2 for the Mingxin FX100, the 480B model (TP8 configuration) under 16 concurrent loads achieved a TTFT p50 of 7.53s, a 26% reduction compared to the baseline (local single NVMe drive) of 10.17s. This result is easily reproducible in short tests, but the question is: during a 72-hour stress test, will the KV cache space become fragmented? Will RoCEv2 congestion control on the NVMe-oF link cause packet loss and retransmission under sustained traffic?

Mingxin's G3 gate test design addresses this issue: it requires that metrics—TTFT reduction ≥25%, throughput improvement +29–40%—remain fully compliant throughout 72 hours of continuous load, with no performance inflection points. The engineering judgment implicit in this design is that if the storage system can maintain a flat performance curve over 72 hours, its behavior over weeks or months of production is likely predictable.

Key Efficiency Optimization Insights from Long-Duration Load Measurements

In the measured report R3 (480B·TP4×2 full-system scope), the FX100 achieved a throughput gain of +35–36%. More notably, this test recorded hourly TTFT and throughput data during the 72-hour stability phase: across 72 sampling points, the coefficient of variation (CV) for TTFT was below 5%, and throughput fluctuation was within ±3%.

This result stems from the synergy of multiple efficiency optimization measures:

• Hierarchical KV Cache Management: The FX100 uses NVMe-oF to cache hot KV blocks on an all-flash array, while cold blocks are demoted to an SSD pool, avoiding write amplification and GC jitter from local NVMe drives.
• Parallel Read Patch: The LMCache parallel read patch reduced TTFT for cold read scenarios from 37.97s to 9.30s (measured, report R1), with a 5.3x bandwidth improvement—this optimization minimized GPU waiting due to cache misses during prolonged operation.
• Network Flow Control Adaptation: RoCEv2's PFC and ECN configurations triggered no packet loss or retransmission during the 72-hour stress test, indicating that the single-port 100 GbE bandwidth planning and queue depth settings matched the burst characteristics of inference workloads.

For GPU utilization, the value of stability stress testing lies in exposing "invisible bottlenecks." For example, if the storage side triggers TRIM or wear leveling during continuous KV cache writes, I/O latency might jump from 100μs to 2ms, causing GPU SM utilization to plummet from 85% to 40%. Measured data from the FX100 shows that through RAID0 striping on the all-flash array and the low-latency characteristics of NVMe-oF, such latency jitter can be controlled within an acceptable range.

Gate-Based Testing: A Paradigm Shift from "Functional Pass" to "Stability Pass"

Traditional compute center acceptance involves running a benchmark or short stress test, passing if peak metrics are met. However, the uniqueness of inference optimization scenarios is that the benefits of KV cache acceleration heavily depend on workload locality. In short tests, the cache may be in a "warm-up" state with high hit rates; in contrast, a 72-hour stress test goes through multiple phases—cold start, cache fill, steady-state operation, cache invalidation—more closely resembling real production.

Mingxin's collaborative model proposes an "approximately 10-week gate-based joint test" to address this: G1 arrival acceptance, G2 single-node baseline, G3 main gate (TTFT reduction ≥25%, throughput +29–40% measured in-band), G4 72-hour stability. The G4 phase requires the system to show no performance degradation, no fault interruptions, and no resource leaks over 72 hours. This process effectively upgrades acceptance from "functional verification" to "efficacy verification."

For investment decision-makers, the value of gate-based testing lies in reducing deployment risk. Consider a 32-GPU inference cluster: if the storage system experiences a 10% throughput drop after 72 hours, this equates to an annual cost of approximately 3 wasted GPUs (at $20,000 per GPU, roughly $60,000/year). The investment in gate-based testing (test cycles, personnel, equipment) is typically less than 10% of such potential losses.

Conclusion

Efficiency optimization for compute centers should not stop at peak metrics. The 72-hour stability stress test, as the final gate for acceptance, verifies performance consistency under real-world loads—directly determining GPU utilization and inference service SLA achievement. Measured data from the Mingxin FX100 on the AMD MI308X platform shows that through NVMe-oF all-flash arrays and hierarchical KV cache management, stable performance can be maintained over extended loads: TTFT reduction of 26–32% and throughput improvement of 29–40%. For teams planning inference cluster acceptance, it is recommended to include the 72-hour stability test as a core gate and focus on storage latency jitter, cache hit rate changes, and network congestion control performance. For further details on gate-based joint testing solutions, please contact the Mingxin technical team for reproducible testing under NDA.