Almost everyone has likely utilized sophisticated large - scale models such as DeepSeek, Tongyi Qianwen, and Kimi - K2. As users' fascination with AI continues to grow, corporate IT departments are increasingly anxious about ensuring sufficient computing power. In the past, an 8 - GPU server could manage small models. However, it falls short when it comes to supporting DeepSeek's MoE architecture inference model, which boasts hundreds of experts and provides responses within milliseconds.

In this context, super nodes have emerged as a popular solution.

Traditional single 8 - card servers are no longer capable of meeting the current computing power requirements for large - model training and inference. NVIDIA took the lead in developing super node technology. This technology integrates dozens or even hundreds of AI computing chips into a single architecture through high - speed internal interconnections. By doing so, it creates a new Scale - up architecture that achieves a significant leap in computing power. The core advantage of this technology can be summed up in one word: efficiency.

Cost - Efficiency: Through high - speed internal interconnections, super nodes enable each card to operate at full capacity. This eliminates the idle computing power that often occurs in traditional multi - cabinet setups, thereby reducing the waste of GPU cards and electricity.

Space - Efficiency: Previously, running large models required multiple cabinets that could occupy half of a data center room. Now, a single super node can handle AI training and inference tasks, significantly reducing the physical space requirements.

Ease of Use: Super nodes come fully integrated with hardware, interconnection, and management systems. This allows enterprises to deploy large models quickly. In contrast, traditional multi - server setups require separate debugging, network configuration, and heat dissipation solutions. The entire process can take up to six months.

Many CIOs and IT professionals are left puzzled. Super nodes promise cost savings in AI deployment, so why are they still out of reach and unaffordable for many? There's a sense of helplessness, akin to having a brilliant idea but lacking the means to implement it.

Indeed, at present, only a limited number of industries and enterprises can truly afford and effectively utilize super nodes.

The reasons behind this are complex. They include supply disruptions of high - end NVIDIA cards and the limited volume production of domestic GPUs. One often - overlooked factor is that the core logic of super nodes, which is to make AI more cost - effective, seems to be going off track.

In news reports about super nodes, parameters like "thousands of cards" or "X hundred cards per cabinet" are frequently highlighted. The media tends to chase new records, and the public often assumes that larger - scale super nodes with more cards are more advanced.

However, do most enterprises truly require such massive computing power behemoths? Can they recover their investments in hundreds - of - card super nodes through AI applications? These questions demand urgent answers.

Undoubtedly, super nodes with hundreds or even thousands of cards are a testament to the progress of domestic intelligent computing technology. But consider this: when you charge your phone, do you care about the size of the power plant, whether it's nuclear or hydroelectric? You only care about whether your phone charges properly, if the voltage is compatible, if the current is stable, and if it won't damage your phone. These are the practical concerns.

Similarly, when enterprises deploy super nodes, beyond the number of integrated cards, they must also take into account the hidden costs in practical applications:

1. Interruption Losses Due to Increased Failure Rates

The larger the super node, the more internal optical modules, switches, and power nodes it contains. For example, a super node architecture with over 300 cards involves nearly 6,900 optical modules and around 100 switches. In high - density deployment scenarios, a failure in any component can disrupt the entire training task. For AI large - model training, a failure means reloading the model from checkpoints and restarting the training process. This wastes days of time and incurs high electricity costs.

For enterprises, uninterrupted operations and avoiding the need for retraining are more critical than having a few extra cards.

2. Excessive Cost per Token Due to GPU Idleness

There is a consensus in the industry that super nodes have an optimal scale. Beyond this point, increasing the scale further yields limited performance gains but significantly higher costs. Simulation data shows that for a 10 - trillion - parameter model, the optimal scale is around 32 - 64 cards. Blindly pursuing super - large scales with hundreds of cards may result in low computing power utilization, with GPUs idling most of the time. For instance, DeepSeek's official paper recommends 144 H800 cards for the Decode phase. If a super node uses domestic cards with one - third the computing power of H800, 48 or even 32 cards might be optimal in PD separation scenarios.

3. Increased Overall TCO Due to Higher Operational Complexity

Highly integrated super node products demand a high level of professional expertise from operational teams. As the scale increases, the number of internal fault points multiplies, making it difficult for small teams to handle issues independently. For example, NVIDIA initially attempted a two - layer architecture super node, integrating switches into a single machine to expand scale. However, enterprise customers preferred single - layer architectures to reduce fault points and operational complexity. NVIDIA's 256 - card super node also failed to commercialize due to high TCO, which is a critical concern for internet enterprise customers.

Thus, larger - scale super nodes with more cards are not necessarily better. Enterprises need IT infrastructure that not only solves computing power problems but also offers an optimal return on investment (ROI). The development of super node technology must return to its original goal of making AI more cost - effective for enterprises. Solutions tailored to their AI business needs are the optimal choice.

Since simply adding more cards is not the solution, how can super nodes truly become cost - saving tools for enterprises? The industry has provided an answer: return to rationality, pursue scale - demand alignment, and reduce comprehensive costs through open ecosystems.

On one hand, more vendors are focusing on more appropriate scales, such as 32 - card and 64 - card configurations.

As mentioned earlier, beyond the performance - cost sweet spot, the marginal benefits of super nodes decline. Therefore, many domestic vendors are concentrating on practical designs with 32 - 64 cards, offering products that meet enterprise needs. For example, New H3C's UniPoD S80000 enables high - density deployment of 32/64 cards per cabinet, making large - model training and inference affordable for most enterprises. Moreover, its single - layer fully interconnected architecture significantly reduces communication latency and fault points, ensuring business continuity in mainstream model scenarios like MoE. The right scale and higher reliability are key to reducing AI computing costs and improving efficiency.

(New H3C Super Node H3C UniPoD S80000)

On the other hand, open hardware ecosystems are becoming a new engine for cost reduction.

With the maturation of domestic chip ecosystems, open architectures support multi - brand domestic accelerator cards. This breaks the monopoly of overseas high - end cards and provides users with more choices, thereby reducing enterprise computing power costs. Such open super nodes have become crucial for making computing power accessible. For instance, New H3C's super node supports multi - brand domestic accelerator cards and is compatible with mainstream software ecosystems like CUDA, lowering supply chain costs for enterprises.

These practices are expected to drive the popularization of super nodes, transforming them from exclusive products for leading enterprises into inclusive infrastructure for all industries.

It should be noted that the development of super nodes with hundreds or thousands of cards has strategic significance. This technological path drives breakthroughs in underlying technologies such as optical interconnection, liquid cooling, and high - density cabling. It represents China's ambitious pursuit of excellence in the computing power industry.

The pace of AI development often exceeds expectations. Today, 32 cards may seem sufficient, but tomorrow, DeepSeek's new model may incorporate more experts, potentially making super - large super nodes more cost - effective per token. Therefore, planning ahead appropriately and further scaling up super nodes can provide redundancy for future technological iterations.

However, for the vast majority (99%) of ordinary enterprises, a pragmatic and inclusive path is essential. They urgently need cost - controlled, stable, and reliable super node products to address the current computing power shortage for large - model deployment and support AI in productivity scenarios.

Thus, beyond simply showcasing card counts, this more practical and inclusive path is equally important and cannot be overlooked.

The ideal future for industry development is the coexistence of these two paths. There will be computing power giants that shock the world and inclusive products serving all industries. Enterprises won't need to worry about the number of cards inside a super node; they can simply plug it in, run AI, and avoid exorbitant infrastructure bills.

Achieving this goal requires intelligent computing vendors to prioritize enterprise needs, incorporating reliability, cost per token, and total cost of ownership (TCO) into their super node R&D roadmaps. After all, making AI more cost - effective for enterprises is the core value of super node technology and the prerequisite for commercial success.