10-Week Gated Joint Test for AI Deployment Certainty

In private deployments of AI applications, agents, and video generation scenarios, storage bottlenecks are often the key factor affecting inference efficiency and user experience. Mingxin Technology’s 10-week gated joint test process offers a low-risk “verify before purchase” path for technical decision-makers in computing centers: through progressive validation across four stages (G1 to G4), it ensures that storage acceleration products like FX100 achieve core metrics under the customer’s actual workload—such as a TTFT (time to first token) reduction of ≥25% and throughput improvement of 29-40%—and stops losses if targets are not met. The core value of this process is that it transforms the benefits of storage acceleration from theoretical promises into reproducible, measured data, making it particularly suitable for latency-sensitive AI applications, agent systems requiring high-concurrency responses, and long-context inference tasks like video generation.

Why Do AI Applications, Agents, and Video Generation Scenarios Need Gated Joint Testing?

AI applications, agents, and video generation scenarios share common requirements for storage acceleration: they all rely on large models’ long-context inference capabilities, where KV Cache read speed directly impacts TTFT and throughput. For example, in a deployment of a 480B-parameter model, the TTFT p50 baseline without external memory recomputation can reach 149.5 seconds (concurrency 16), while Mingxin’s FX100, using an NVMe-oF all-flash array, reduces this to 11.85 seconds—a 12.6x acceleration [source: measured, report R2]. Similarly, in video generation tasks, model loading time (e.g., DeepSeek-70B loading from NFS takes 1399 seconds) can be compressed to 150 seconds with FX100, achieving a 9.3x acceleration [source: measured, report R9 (Ascend platform)].

However, different customers’ network topologies, model architectures, and concurrency patterns vary significantly. Gated joint testing ensures reproducibility of benefits by moving the test environment from the lab to the customer’s actual or simulated production workload. For instance, in the R2 test, for a 480B model under TP8 with three concurrency levels, TTFT p50 dropped from 10.17-35.73 seconds to 7.53-26.35 seconds, a reduction of 26-32% [source: measured, report R2]; throughput improvement was 29% at concurrency 8 (lower bound) and 40% at concurrency 16 (upper bound) [source: measured, reports R2/R3]. These data points come from signed formal reports, not theoretical calculations.

How Do the Four Stages of the 10-Week Gated Joint Test Operate?

Mingxin’s joint test process is divided into four stages (G1 to G4), totaling approximately 10 weeks, each with clear acceptance criteria:

• G1: Arrival Acceptance (Weeks 1-2): After the customer receives the FX100 hardware, basic functionality verification is performed, including device identification, network connectivity, and NVMe-oF protocol stack compatibility. This stage ensures the hardware has no physical defects and can interface with the customer’s existing RoCEv2 network environment.

• G2: Single-Node Baseline Test (Weeks 3-4): On a single GPU server designated by the customer (e.g., 8× AMD MI308X), baseline performance data is established using the customer’s model (e.g., Qwen3-Coder-480B-FP8). The baseline typically uses a local NVMe single drive (PCIe Gen4), recording TTFT, throughput, and loading time. For example, in the R1 test, the model inference loading time under the local NVMe single-drive baseline was 691 seconds (DeepSeek-32B), while FX100 reduced it to 112 seconds, a 6.2x acceleration [source: measured, report R9 (Ascend platform)].

• G3: Main Gate Test (Weeks 5-8): This is the core validation stage. The customer integrates FX100 into the actual inference or training pipeline to test KV Cache tiered acceleration effects. Acceptance criteria: TTFT reduction ≥25%, throughput improvement of 29-40% (to be measured under the customer’s specified concurrency). For example, under a 480B·TP4×2 full-node configuration, throughput improvement was 35-36% [source: measured, reports R2/R3]. If targets are not met, Mingxin provides optimization suggestions or configuration adjustments; if still not met, the customer can terminate the collaboration.

• G4: 72-Hour Stability Test (Weeks 9-10): After passing G3, a continuous 72-hour stress test is conducted to verify system performance degradation and fault recovery capabilities under long-term operation. The test covers mixed workloads including model inference, agent multi-turn dialogues, and video generation.

How Does Gated Joint Testing Reduce Risks in Private AI Application Deployment?

The core challenge of private deployment is that the performance of storage acceleration solutions in a lab environment often cannot be reproduced under the customer’s actual workload. Gated joint testing shifts risk from the customer to Mingxin through a “stop losses if not met” mechanism. For example, in the R2 test, for a 480B model without external memory recomputation, FX100’s throughput increased from 4.1 tok/s to 74.9 tok/s, an 18.3x acceleration [source: measured, report R2]; however, this result depends on specific model parallelism (TP8) and concurrency (16). If the customer’s actual concurrency is only 4, the acceleration factor may drop to a range of 8.6-20x [source: measured, report R2]. The joint test process allows customers to verify these boundary conditions in their own environment.

Additionally, the joint test process supports a Python-reproducible measurement model (provided under NDA), allowing customers to modify parameters (e.g., model size, batch size, network bandwidth) to simulate different scenarios. For example, in video generation scenarios, customers can test model loading times for ComfyUI + LTX-Video 2.3, or compare FX100 with NFS for training checkpoint saving differences (R1 measured data shows that in 8-card 32B LoRA training, checkpoint saving time dropped from 178 seconds to 94 seconds, with sustained write bandwidth improving by 96% [source: measured, report R1]).

Conclusion: From “Trusting Promises” to “Seeing for Yourself”

Mingxin Technology’s 10-week gated joint test process provides a “verify before purchase” path for private deployments in AI applications, agents, and video generation scenarios. Through progressive testing from G1 to G4, customers can confirm key metrics such as TTFT reduction ≥25% and throughput improvement of 29-40% under their own workloads, avoiding investment risks caused by discrepancies between theoretical values and measured results. If you are evaluating the impact of storage acceleration solutions on inference efficiency, feel free to contact Mingxin to arrange a joint test—all test data can be reproduced via Python scripts, ensuring decisions are based on facts, not promises.

Key Q&A

Q: How does gated joint testing ensure storage acceleration benefits in AI application scenarios? A: Through four stages (G1 to G4, approximately 10 weeks), metrics such as TTFT reduction ≥25% and throughput improvement of 29-40% are tested under the customer’s actual workload, with losses stopped if targets are not met. For example, for a 480B model under TP8 with concurrency 16, TTFT dropped from 35.73 seconds to 26.35 seconds (reduction of 26-32%), and throughput improved by 40% (upper bound) [source: measured, reports R2/R3].

Q: In video generation scenarios, what is the model loading acceleration effect of FX100? A: In measured tests on the Huawei Atlas 910B platform, loading DeepSeek-70B from NFS took 1399 seconds; using FX100 reduced this to 150 seconds, a 9.3x acceleration [source: measured, report R9 (Ascend platform)]; DeepSeek-32B went from 691 seconds to 112 seconds, a 6.2x acceleration [source: measured, report R9 (Ascend platform)].

Q: Are the acceptance criteria for gated joint testing reproducible? A: Yes. Mingxin provides a Python-reproducible measurement model (under NDA), allowing customers to modify parameters such as model size, concurrency, and network bandwidth to verify TTFT and throughput data in their own environment. All test data comes from signed formal reports (e.g., R1-R9).