Test Before You Decide: The Complete 10-Week Gated Joint Testing Process

As AI applications transition from experimentation to production, the need for storage acceleration in scenarios such as agents and video generation becomes increasingly clear: KV cache management for long-context inference, latency control for model loading, and write efficiency for training checkpoints directly determine whether a system can run stably in private deployments. However, traditional procurement processes often rely on paper specifications or short-term demonstrations, making it difficult to expose performance bottlenecks under real workloads. To address this pain point, Mingxin’s FX100 product line offers a 10-week gated joint testing process based on a “test before you decide” approach, designed to reduce selection risk through standardized testing.

Design Logic of Gated Joint Testing: Four-Stage Validation from G1 to G4

The joint testing process is divided into four gated stages (G1 to G4), each with clear acceptance criteria to ensure reproducible, measured data is obtained before committing funds. The entire cycle takes approximately 10 weeks, covering the full chain from hardware delivery to stability validation.

• G1: Arrival Acceptance (Weeks 1–2)
  After receiving the Mingxin FX100 test unit (e.g., FX100 all-flash NVMe-oF array with 4-disk RAID0 and RoCEv2 networking), the customer performs basic hardware checks and firmware version confirmation. This stage involves no performance testing, only verifying device integrity.

• G2: Single-Node Baseline Testing (Weeks 3–4)
  In the customer’s designated private deployment environment, a test platform is set up (reference configuration: 8× AMD Instinct MI308X GPUs, 2× AMD EPYC 9654 CPUs, ~1.5 TB memory) and standard workload models (e.g., Qwen3-Coder-480B-FP8) are run. Baseline testing compares performance differences between a local NVMe single drive (PCIe Gen4, 2 TB) and the FX100 array in scenarios such as KV cache acceleration and model loading. For example, measured, report R2 shows that with the 480B model in TP8 configuration, the FX100 reduces first-token latency (TTFT p50) from 10.17–35.73 s to 7.53–26.35 s, a reduction of 26–32% [source: measured, report R2]. This stage outputs a baseline report for the customer to decide whether to proceed to the next gate.

• G3: Main Gate Testing (Weeks 5–8)
  This is the core validation phase, requiring a TTFT reduction of ≥25% and throughput improvement of +29–40% (in-band measured). For example, in agent scenarios under long-context cold recovery workloads, the FX100 achieves a 29% throughput improvement at concurrency level 8 and 40% at concurrency level 16 [source: measured, reports R2/R3]. For scenarios requiring frequent model loading, such as video generation, the FX100 on the Huawei Atlas 910B platform reduces the DeepSeek-70B service loading time from 1399 s to 150 s, achieving a speedup of 9.3× [source: measured, report R9 (Ascend platform)]. All test data is provided under an NDA framework with Python reproducible scripts to ensure transparency.

• G4: 72-Hour Stability Testing (Weeks 9–10)
  Under the customer’s actual workload (e.g., mixed agent requests and video generation tasks), the system runs continuously for 72 hours, monitoring IOPS, latency jitter, and bandwidth stability. If any metric deviates from the G3 baseline by more than 10%, it is considered non-compliant, and Mingxin bears the cost of device return.

Data-Driven Decision-Making: KV Acceleration and Training Efficiency

The core value of the gated joint testing process is replacing subjective judgment with measured data. The test results from the following two scenarios can serve as selection references:

• KV Cache Acceleration: In long-context inference, the FX100 offloads KV cache to an all-flash array via the NVMe-oF architecture. Report R2 testing shows that without external memory recomputation, the FX100 achieves a TTFT p50 of 11.85 s, with an acceleration factor of 8.6–20× compared to the recomputation baseline (149.5 s); throughput increases from 4.1 tok/s to 74.9 tok/s [source: measured, report R2]. For agent applications (e.g., context management in multi-turn conversations), this capability significantly reduces user wait times.

• Training Checkpoint Saving: In an 8-GPU 32B LoRA training scenario, the saving time for each 65.6 GB full model snapshot decreases from 178 s to 94 s, and sustained write bandwidth increases from 3.26 GB/s to 6.40 GB/s (+96%) [source: measured, report R1]. For iterative training of video generation models, this means shorter downtime windows, improving overall resource utilization.

Common Issues in Private Deployments and Joint Testing Adaptation

In private deployments, customers often encounter issues such as network topology differences and storage protocol compatibility. Mingxin’s joint testing process allows customers to adjust configurations in their own environment, for example:

• Network and Protocol: The FX100 supports RoCEv2 and standard NVMe-oF, operating on existing Ethernet infrastructure without the need for dedicated Fibre Channel.
• Model Adaptation: Testing covers mainstream frameworks (e.g., vLLM 0.20.1+rocm721, LMCache). Customers can submit custom models (e.g., agent-specific MoE architectures or video generation models like LTX-Video 2.3) for adaptation testing, with Mingxin providing code export packages and debugging support [source: measured, reports R6/R7].
• Scalability Validation: For multi-node scenarios, the G3 phase can increase concurrency loads (e.g., TP4×2 full-machine configuration) to verify performance consistency of the FX100 in distributed inference.

Conclusion

The 10-week gated joint testing process transforms procurement decisions from “parameter comparison” to “measurement-driven” through phased validation, making it especially suitable for AI applications, agents, and video generation scenarios sensitive to KV acceleration and model loading latency. Mingxin’s FX100 product line (FX100 to FX400) has undergone multiple rounds of testing on AMD and Huawei platforms, with reproducible data. If you are evaluating storage acceleration solutions for private deployments, feel free to contact the Mingxin technical team to initiate joint testing and verify real-world benefits through the gated process.