Second Supply Source Strategy: Why AI Companies Need Backup Computing Channels
As AI applications accelerate their deployment, enterprises face an increasingly acute contradiction: the stronger the model capability, the deeper the dependence on computing infrastructure. Whether it is supporting real-time decision-making for agents, frame-level rendering for video generation models, or inference throughput in private deployments, computing interruptions or performance bottlenecks can directly translate into business losses. However, the computing supply chains of most AI companies remain highly concentrated among a few cloud service providers or hardware vendors. This single-source dependency is evolving from a cost optimization issue into a business continuity risk.
Backup computing channels are not simply about "buying a few more machines," but rather a systematic supply strategy. It requires enterprises to build elasticity into their computing procurement, architecture adaptation, and operational systems, ensuring seamless switching when the primary channel faces price fluctuations, performance bottlenecks, or supply shortages. This article explores the necessity and feasibility of a second supply source strategy from three dimensions: performance validation, cost structure, and application scenarios.
Performance Validation: Baseline Testing of Backup Channels
Selecting a backup computing channel first requires answering a core question: can the alternative solution achieve or approach the performance of the primary solution on critical workloads? Taking the measured performance of Mingxin's FX100 all-flash NVMe-oF array on the AMD MI308X platform as an example, its performance in large model inference scenarios provides quantifiable reference.
In the KV Cache acceleration test for a 480B parameter model, at the optimal operating point with 16 concurrent streams, the FX100 improved throughput by 40% (upper bound) and reduced time-to-first-token (TTFT) by 26–32% (measured, report R2). More critically, in the extreme scenario without external memory recomputation, the FX100 achieved an acceleration factor of 8.6–20x—the recomputation baseline TTFT p50 was 149.5 seconds, while the FX100 required only 11.85 seconds (measured, report R2). This means that for long-context inference, if a backup computing channel possesses similar storage acceleration capabilities, it can reduce the latency caused by forced recomputation due to insufficient memory by an order of magnitude.
For latency-sensitive workloads such as video generation or agents, the IOPS and bandwidth metrics of backup channels are equally critical. The FX100 provides 16M IOPS over a PCIe 3.0 interface, while the FX200 and FX300 achieve 32M and 60M IOPS, respectively (vendor specification). These figures indicate that even if the primary channel uses more advanced PCIe 5.0 or 6.0 devices, a backup channel with predictable storage acceleration performance can avoid a "performance cliff" during switching.
Cost and Supply Resilience: The Economics of Backup Channels
Another driver for backup computing channels is cost volatility. The supply chain for AI chips and storage devices is heavily influenced by geopolitical factors, capacity cycles, and market demand. For example, the price of the NVIDIA H100 experienced dramatic fluctuations from scarcity to surplus between 2023 and 2024. Enterprises relying on a single source were forced to accept premiums during price peaks and faced project delays during supply shortages.
The reference pricing for Mingxin's FX product line provides a comparison baseline: a fully configured FX100 system is approximately ¥371,200 (approx. ¥2,014/TB), the FX200 is approximately ¥331,200 (approx. ¥1,797/TB), and the FX300 is approximately ¥924,000 (approx. ¥5,014/TB) (vendor specification). While these prices are not the lowest, they represent the cost range for domestic computing across PCIe 3.0 to 5.0 generations. For private deployment scenarios, the procurement cost of backup channels can be amortized across multiple project cycles, avoiding the ongoing rental fees of cloud services.
More importantly, backup channels offer supply resilience. Mingxin's FX400, planned for mass production by the end of 2026, will feature a PCIe 6.0 interface and E1.S form factor (factual list), providing a generational upgrade path for long-term planning. If AI companies establish partnerships with vendors possessing independent supply chains, in addition to their primary channel, they can maintain business continuity when the primary channel is disrupted. For example, tests on the Huawei Atlas 910B platform showed that the FX100 reduced the service loading time for DeepSeek-70B from 1399 seconds (NFS baseline) to 150 seconds (measured, report R9, Ascend platform), achieving a speedup of 9.3x. This demonstrates that even on non-mainstream GPU platforms, storage acceleration from backup channels can significantly shorten model deployment time.
Application Scenarios: Backup Needs from Agents to Video Generation
Different AI applications have varying requirements for backup computing, but the core logic remains consistent: avoiding single points of failure.
Agent Scenario: Agents typically require high-frequency inference calls and low-latency responses. For instance, a multi-agent collaboration system might simultaneously maintain hundreds of conversation contexts, each requiring fast read/write access to KV Cache. If the primary computing power encounters an I/O bottleneck, agent response times could degrade from milliseconds to seconds, leading to a collapse in user experience. A backup channel capable of providing similar parallel read capabilities at the storage layer can maintain service quality during switching. In measured tests, with the LMCache parallel read patch, the FX100 reduced the TTFT for single-card cold reads from 37.97 seconds to 9.30 seconds, improving bandwidth by 5.3x (measured, report R1). This means that storage acceleration from a backup channel can serve as a "safety net" for agent systems.
Video Generation Scenario: Video generation models (e.g., LTX-Video 2.3) have extremely high demands on VRAM and storage bandwidth. In ComfyUI full model deployment tests, the FX100's NVMe-oF array supported multi-frame parallel rendering, avoiding model offloading triggered by insufficient VRAM (measured, reports R6/R7). For privately deployed video generation services, the storage acceleration capability of a backup channel directly determines whether high-resolution video models can run locally, rather than relying on cloud GPU clusters.
Private Deployment Scenario: The core requirements of private deployment are data security and low latency. In this scenario, backup computing channels play a dual role: first, as a "cold standby" for the primary computing power, taking over workloads when the primary device fails; second, as a "hot standby," dynamically switching part of the workload when the primary computing power becomes too expensive. In training checkpoint saving, the Mingxin FX100 reduced the save time for 8-card 32B LoRA from 178 seconds to 94 seconds, improving sustained write bandwidth by 96% (measured, report R1). For private deployments requiring frequent training state saves, the write performance of a backup channel can significantly reduce training interruption time.
Conclusion
The second supply source strategy is not a rejection of the primary channel, but rather an active management of uncertainty in the computing supply chain. From performance validation to cost analysis, and to adaptation for specific application scenarios, a backup channel needs quantifiable performance benchmarks, a reasonable cost structure, and compatibility with existing architectures. The measured data from Mingxin's FX series in scenarios such as KV Cache acceleration, model loading, and training snapshots demonstrates the practical capabilities of domestic computing in the field of storage acceleration. For AI companies planning their computing infrastructure, it is recommended to validate the performance boundaries of backup channels through gated joint testing (e.g., Mingxin's approximately 10-week testing process), ensuring that business operations do not halt due to computing interruptions when a switch is necessary.