The Rise of Intelligent Computing Centers: Seven Key Considerations for Selecting the Right GPUs
In today’s rapidly advancing technological landscape, the construction of intelligent computing centers has become a driving force behind innovation across industries.
In recent discussions with peers in the intelligent computing industry, the “investment, construction, and operation” lifecycle has been a recurring theme—particularly the topic of “GPUs,” which has emerged as a top concern.
This growing interest is primarily driven by three factors:
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The Core of AI Computing: Modern intelligent workloads rely heavily on GPUs and other AI-specific chips.
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Budget Dominance: GPUs account for approximately 70–80% of the total product cost in intelligent computing centers—and within GPU servers, the chips themselves take up 70–80% of the cost.
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Scenario-Specific Requirements: Different centers, use cases, and workloads require tailored GPU solutions—no one-size-fits-all option exists.
Let’s explore seven critical aspects to consider when selecting GPUs for intelligent computing centers.
1. Performance vs. Price: Finding the Optimal Balance
In the design and deployment of intelligent computing infrastructure, delivering cost-effective computing power is a top priority.
From our experience, we begin by benchmarking the market’s leading GPUs and calculating the cost per petaflop (P) of FP16 dense compute performance. These data points help clients make informed decisions.
For example, many clients are torn between the NVIDIA A100 and H100. Upon closer analysis, the H100 delivers 1P FP16 dense compute at roughly ¥250,000 per card, whereas the A100 offers about 0.3P at half the price. This makes the H100 more cost-effective in many scenarios.
It’s worth noting that GPU prices fluctuate like stock indices. To assist with ongoing tracking, we’ve compiled a reference tool covering over ten mainstream NVIDIA GPU models—details can be found at the end of this article.
2. Product Leadership: Ensuring Long-Term Competitiveness
Although next-gen GPUs like the B200 have not yet entered large-scale production, procurement requests for these cards have already surfaced in the market—highlighting a widespread hunger for performance leadership.
From a technical standpoint, the B200 delivers up to 2.25P of FP16 dense compute per card—more than double the performance of current H100 or H200 models.
Considering that intelligent computing centers often have a life cycle of 3–5 years, selecting GPUs with future-ready capabilities is crucial to maintaining competitiveness as chip technologies evolve rapidly.
3. Ecosystem Maturity: The Often-Overlooked Factor
While NVIDIA currently dominates the GPU landscape, there are other players offering compelling alternatives—such as AMD, Intel, and several emerging chip manufacturers focused on AI acceleration.
However, technical specifications alone don’t guarantee success. Ecosystem maturity—the availability of developer tools, compatibility, community support, and software frameworks—is often the deciding factor.
Take AMD’s MI325X or Intel’s Gaudi2: both offer strong raw specs comparable to the H100. Yet, they struggle with adoption due to a less mature software ecosystem compared to NVIDIA’s CUDA. This results in steeper learning curves and integration complexity—making them less viable for large-scale compute leasing services or standardized deployment.
4. Workload Fit: Matching GPUs to the Right Use Case
Intelligent computing centers focus heavily on tasks such as large model training, fine-tuning, and inference. But no single GPU can dominate across all use cases.
Even high-performance GPUs like the H100 may not be the most cost-efficient option for inference tasks—many data centers are now turning to RTX 4090 or 4090D GPUs for this stage due to better economics.
A clear division has emerged:
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Training workloads → H100 / H200
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Inference workloads → RTX 4090 / 4090D
Carefully matching GPU types to workload profiles ensures optimal performance and resource efficiency.
5. Emerging Alternatives: The Rise of Non-Mainstream Chips
With the accelerated global development of AI chips, we are seeing a rise in the adoption of alternative GPU solutions in intelligent computing center deployments.
Recent data indicates that non-mainstream AI chips now account for 20% of AI accelerator shipments—up from just 10% the year prior.
This shift is supported by favorable policy environments and growing industry demand for diversified chip ecosystems. As a result, many intelligent computing centers are beginning to deploy AI chips beyond the traditional market leaders.
In some regions, these deployments are rapidly scaling. Additionally, growing sentiment around reducing reliance on specific vendors is accelerating the demand for diversified GPU strategies and alternative chip architectures.
6. Cluster Scalability: Stress Testing at Scale
Most intelligent computing centers today operate clusters ranging from thousands to tens of thousands of GPUs. In contrast, some global projects, such as Elon Musk’s xAI, have deployed 100,000+ GPU clusters based on NVIDIA’s H100.
This raises a critical question: Are alternative GPU providers ready for deployments at that scale?
While non-mainstream chips have reached relative maturity in 1,000-card deployments, large-scale (10,000+ card) production clusters with high utilization rates are still uncommon. This means evaluating cluster scalability and interconnect architecture is essential during GPU selection.
7. Supply Chain Readiness: The Foundation of Deployment Success
No matter how powerful or cost-effective a GPU might be, the project fails if supply cannot keep pace with deployment schedules.
Project bidding documents often stipulate delivery timelines, and delays in GPU shipment can result in missed opportunities—especially when competing for renewable energy credits or green electricity quotas that favor early deployment.
Due to foundry constraints, many chips still rely on third-party manufacturing, which introduces risk. Overcoming these bottlenecks will require collaborative innovation and supply chain coordination across the entire industry.
Final Thoughts: Toward High-Efficiency AI Infrastructure
Selecting the right GPUs for an intelligent computing center is a highly complex and strategic process. Decision-makers must evaluate performance, pricing, product maturity, ecosystem compatibility, workload fit, deployment scalability, and supply chain robustness—all at once.
By making thoughtful, data-driven decisions, organizations can lay a strong foundation for reliable, efficient, and forward-looking AI infrastructure—positioning themselves to lead in a future powered by intelligent computing.
We hope this guide offers practical value to those navigating the world of GPU selection. As intelligent computing centers evolve, their impact on technological innovation and economic development will only grow. Let’s build that future—together.
