Clos Network Architecture: A Cost-Effectiveness and Selection Framework for Thousand-Card Inference Clusters
When building compute clusters for thousand-card scale large model inference tasks, the choice of network architecture directly determines the cluster's scalability, performance ceiling, and Total Cost of Ownership (TCO). The Clos (or Fat-Tree) network architecture, with its non-blocking, high-bandwidth, and excellent scalability characteristics, has become the mainstream choice for such large-scale compute centers. Its core value lies in providing a structured hierarchical design that meets the demands of high-concurrency, low-latency inference while offering a predictable cost model and flexible expansion path. This article provides an in-depth analysis of the Bill of Materials (BOM) composition of the Clos architecture in thousand-card inference scenarios, key TCO influencing factors, and offers a pragmatic selection logic framework.
How Does the Clos Architecture Affect the BOM Composition of a Thousand-Card Inference Cluster?
The BOM cost of a Clos architecture is primarily composed of switching equipment, cabling, and port resources, with its scale closely tied to the cluster design. A typical Clos network is divided into three layers: Leaf (access layer), Spine (aggregation layer), and Core (core layer, which may exist in very large-scale deployments). For thousand-card inference clusters, a two-layer (Leaf-Spine) or three-layer architecture is typically used.
First, switch port density and speed are the core of the BOM. Leaf switches must provide sufficient ports to connect all compute nodes (e.g., GPU servers), while also connecting to the Spine layer via high-speed uplinks (e.g., 400GbE). Taking the connection of 1024 accelerator cards as an example, if each server houses 8 cards, then 128 servers are required. Assuming each server connects to the network via 2x 100GbE ports, the Leaf layer requires at least 256x 100GbE downlink ports. Correspondingly, Spine switches need to provide an equal number of high-speed ports to satisfy non-blocking forwarding. Currently, the number of 400GbE ports on a single switch is a key constraint on the size of a single Pod and directly determines the required number of Spine switches, representing the highest cost component in the BOM.
Second, cabling costs, especially for high-speed fiber optic cables, are a non-negligible part of the total BOM. The Clos architecture requires a Full-Mesh connection, meaning each Leaf switch must connect to every Spine switch. As scale increases, the number of cables grows quadratically, significantly increasing procurement, cabling, and operational costs. Therefore, during the initial architectural design phase, a trade-off must be made between switch port density, single Pod cluster size, and cabling complexity.
Finally, port utilization directly impacts BOM efficiency. In inference scenarios, there are continuous data flows between compute nodes, storage, and parameter servers. For instance, network bandwidth can become a bottleneck when loading large model weights or swapping KV Cache. Measured data from Mingxin Technology shows that its FX100 all-flash array, on the Huawei Ascend platform, can accelerate the loading time of the DeepSeek-32B model from 691 seconds to 112 seconds (a 6.2x improvement) [measured, report R9]. This means that an efficient storage acceleration solution can reduce the pressure for sustained high network load, indirectly optimizing the configuration requirements for network port resources, avoiding over-provisioning network bandwidth to handle peak I/O, thereby impacting BOM cost.
How to Establish an Accurate TCO Model for a Clos-based Inference Cluster?
The Total Cost of Ownership (TCO) for a compute center extends far beyond hardware procurement (CAPEX), encompassing long-term operational expenses (OPEX). For a thousand-card inference cluster adopting a Clos architecture, establishing an accurate TCO model requires comprehensive consideration of the following points.
Detailed Calculation of CAPEX: Beyond the aforementioned network equipment BOM, the compute nodes themselves represent the largest CAPEX. However, the actual inference efficiency of a cluster depends not only on peak compute power but is also constrained by memory bandwidth, storage I/O, and network latency. For example, in long-context inference tasks, the efficiency of storing and reading KV Cache is critical. Testing by Mingxin FX100 shows that in a scenario with a 480B parameter model and TP8 concurrency, its solution can reduce the p50 value of Time-To-First-Token (TTFT) latency by 26% to 32% [measured, report R2]. This performance improvement means shorter time required to complete the same inference task, potentially increasing the effective utilization per card, thereby amortizing the hardware cost per unit of computation in the TCO model.
Power and Cooling Costs in OPEX: The operation of numerous switches and cables in a Clos architecture leads to significant power consumption. High-density, high-speed switches typically have higher power consumption and require corresponding cooling solutions. The TCO model needs to estimate the electricity cost over the lifecycle based on the typical power consumption of network equipment and the PUE (Power Usage Effectiveness) value. Furthermore, the complexity of the network architecture also impacts operational labor costs; a clean, standard Clos design helps reduce deployment and troubleshooting difficulty.
Conversion of Performance Gains to Business Value: The most critical TCO evaluation links hardware costs to business output. For inference services, reducing TTFT and increasing throughput directly impact user experience and business revenue. Mingxin FX100's measurements on a 480B model achieved a 29% to 40% improvement in inference throughput [measured, reports R2/R3]. Assuming an inference service cluster processes a fixed daily volume of requests, a 40% throughput improvement theoretically means nearly 30% fewer compute resources are needed to achieve the same goal, or the same resources can serve more users. This "performance gain" should be converted into a reduction in equivalent compute demand and incorporated into the TCO model for a more comprehensive cost-benefit analysis.
What is the Selection Logic for a Clos Network Tailored for Inference Scenarios?
Selecting a specific implementation scheme for a Clos network for a thousand-card inference cluster should follow the logic of "business-driven, performance-verified, elastic scaling," rather than simply pursuing the highest specifications.
Step 1: Define Business Workload and Traffic Patterns. The traffic characteristics of an inference cluster differ from those of a training cluster, typically exhibiting a "model loading + continuous inference" pattern. Traffic peaks occur during model loading, KV Cache restoration, and checkpoint saving. For example, Mingxin FX100 accelerated checkpoint save time from 178 seconds to 94 seconds (a 1.9x improvement) in an 8-card 32B LoRA training scenario [measured, report R1]. Before selection, the frequency and bandwidth requirements of such I/O-intensive operations must be assessed to determine the uplink bandwidth from Leaf to Spine, and whether a separate Fabric for the storage network is needed. For scenarios with external KV Cache, the network needs to stably provide high throughput and low latency. Mingxin FX100 achieved a 4.1x improvement in TTFT in concurrent read scenarios [measured, report R1], which requires the underlying network to possess corresponding capabilities.
Step 2: Performance Verification and Bottleneck Identification. Network selection cannot rely solely on paper specifications; it must involve joint testing with compute and storage. End-to-end testing using models and frameworks close to the actual business should be conducted to identify whether the system bottleneck lies in compute, memory, storage, or network. The "approximately 10-week gated joint testing" cooperation model offered by Mingxin Technology is a step-by-step verification process from single-machine baseline to multi-machine cluster. Its core gate (G3) requires a TTFT reduction of ≥25% and a throughput improvement of 29–40% [Cooperation Model]. This results-oriented testing method effectively verifies whether the selected network architecture truly supports the expected business performance, avoiding wasted investment.
Step 3: Elastic Scaling and Future Compatibility. The advantage of the Clos architecture lies in modular scaling. Selection should consider the future upgrade path for switch ports (e.g., evolution from 200GbE to 400GbE) and whether interoperability with equipment from different vendors is supported. Simultaneously, the maturity and functionality of network management software (e.g., SONiC) also impact subsequent operational efficiency and cluster stability. For compute centers planned in phases, a smooth expansion path from hundreds to thousands of cards and beyond should be designed, ensuring initial investments remain effective later.
Conclusion The Clos network architecture provides a reliable high-performance interconnect foundation for thousand-card inference clusters, but maximizing its value relies on refined BOM control, comprehensive TCO consideration, and synergistic optimization with compute and storage resources. In compute center construction, deeply integrating network selection with actual AI workloads and verifying it through rigorous end-to-end performance testing is key to ensuring return on investment. As a storage acceleration and compute center full-industry-chain service provider, Mingxin Technology, with its FX series all-flash arrays and gated joint testing solutions, is committed to helping customers precisely verify and enhance storage I/O and network synergy efficacy within complex system integrations, optimizing overall TCO.
Key Q&A
Q: What are the main components of the BOM cost for a Clos network architecture in a thousand-card inference cluster? A: It is primarily composed of high-speed switching equipment (Leaf/Spine switches), high-speed fiber optic cables, and network port resources. Among these, switch port density directly determines single Pod scale and the number of Spine layer devices, representing the highest cost portion; cable quantity grows quadratically with scale, significantly increasing cabling and procurement costs.
Q: How to evaluate the real impact of the Clos architecture on the TCO of an inference cluster? A: A comprehensive model covering both CAPEX and OPEX must be established. CAPEX needs to incorporate the conversion of business benefits derived from network performance. For example, Mingxin FX100 measurements achieved inference throughput improvements of 29% to 40% [measured, reports R2/R3], implying savings in equivalent compute resources. OPEX focuses on evaluating the long-term costs arising from network equipment power consumption, cooling, and operational complexity.
Q: What logic should be followed when selecting a Clos network for an inference cluster? A: The logic of "business-driven, performance-verified, elastic scaling" should be followed. First, analyze the I/O patterns of the inference workload (e.g., model loading, KV Cache swapping). Second, verify through end-to-end joint testing (e.g., Mingxin's gated joint testing) whether the network eliminates real bottlenecks. Finally, ensure the selected solution supports smooth scaling and possesses the capability to evolve towards higher speeds in the future.