In late 2025, news of ByteDance's massive investment plan to procure tens of thousands of Nvidia's top-tier AI chips captured the tech world's attention. While media narratives focused on capital rivalry and geopolitics, a far more monumental and complex engineering challenge was quietly overlooked: transforming these chips into usable, efficient, and stable computing power is vastly more difficult than acquiring them. As chip counts scale from hundreds in labs to tens of thousands for industrial use, system design complexity doesn't grow linearly—it undergoes a qualitative leap. The floating-point capability of a single GPU is no longer the bottleneck. How to achieve ultra-high-speed communication between chips, supply massive training datasets at millisecond latency, efficiently distribute and cool immense power loads, and intelligently schedule thousands of computing tasks—these systemic questions constitute an engineering chasm between raw hardware and AI productivity.

This analysis moves beyond capital-driven narratives to delve into the engineering core of building ten-thousand-GPU clusters. The focus is not on which chips companies purchase, but on how these chips are organized, connected, and managed to form an organic whole. From the hardware interconnects within server racks that dictate the performance ceiling, to the software "brain" coordinating everything at data-center scale, to the resilient architecture pre-designed for supply chain uncertainties—this reveals that the core of AI competition has quietly shifted from algorithmic innovation to absolute mastery over foundational infrastructure.

Networking and Storage: The Invisible Performance Ceiling

In a ten-thousand-card cluster, the peak computational power of a single GPU is merely theoretical; its actual output is entirely constrained by the speed at which it receives instructions and data. Therefore, network interconnects and storage systems form the most critical invisible ceiling for the entire system. On the networking front, standard Ethernet is insufficient, necessitating high-bandwidth, low-latency solutions like InfiniBand or dedicated NVLink networks. Engineers face a crucial first decision: choosing the network topology. Should they opt for a traditional fat-tree topology guaranteeing equal bandwidth between any two points, or a more cost-effective but potentially congested Dragonfly+ topology for certain traffic patterns? This choice directly impacts the efficiency of gradient synchronization in large-scale distributed training, thereby dictating model iteration speed.

Parallel to networking is the storage challenge. Training a large language model may require reading datasets ranging from hundreds of terabytes to petabytes. If storage I/O speed cannot keep pace with GPU consumption, the majority of expensive chips will idle in a state of "starvation." Consequently, the storage system must be designed as a distributed parallel file system supported by all-flash arrays, utilizing RDMA technology to enable GPUs to communicate directly with storage nodes—bypassing CPU and OS overhead for direct memory access. Going further, it requires configuring large-scale, high-speed local caches on compute nodes. Using intelligent prefetching algorithms, data anticipated for use is loaded in advance from central storage onto local NVMe drives, forming a three-tier data supply pipeline: "central storage – local cache – GPU memory." This ensures computational units remain continuously saturated. The co-design of network and storage aims to make data flow like blood—with sufficient pressure and speed—continuously nourishing every compute unit.

Scheduling and Orchestration: The Cluster's Software Brain

Hardware forms the cluster's body, while the scheduling and orchestration system is the software brain that gives it intelligence and soul. Once tens of thousands of GPUs and their associated CPU and memory resources are pooled, efficiently, fairly, and reliably distributing thousands of AI training and inference tasks of varying sizes and priorities becomes an immensely complex combinatorial optimization problem. Open-source Kubernetes serves as the foundational layer with its powerful container orchestration, but fine-grained management of heterogeneous resources like GPUs requires extensions such as NVIDIA DGX Cloud Stack or KubeFlow. The scheduler's core algorithm must consider multidimensional constraints: not just GPU count, but also GPU memory size, CPU core count, system memory capacity, and even a task's specific network bandwidth or topology affinity requirements.

A more complex challenge lies in fault tolerance and elastic scaling. In a system composed of tens of thousands of components, hardware failure is the norm, not the exception. The scheduling system must monitor node health in real-time. Upon detecting a GPU error or node failure, it must automatically evict affected tasks from the faulty node, reschedule them on healthy nodes, and resume training from the last checkpoint—all transparently to the user. Simultaneously, facing sudden surges in inference traffic, the system should, based on policy, automatically "preempt" GPU resources from the training task pool to elastically and rapidly scale up inference services, releasing them back once traffic subsides. The intelligence level of this software brain directly determines the cluster's overall utilization rate. This is the critical conversion rate turning massive capital expenditure into effective AI output—a value arguably on par with the performance of the chips themselves.

Resilience and Sustainability: Architecture for Uncertainty

Against a backdrop of technological controls and geopolitical volatility, the architecture of a ten-thousand-GPU cluster must also be infused with the gene of "resilience." This means the infrastructure cannot be designed as a fragile monolith dependent on a single vendor, region, or technology stack. It must possess the ability to continuously evolve and withstand risks under constraints. The first step is pursuing diversification at the hardware level. While pursuing peak performance, the architecture should consider compatibility with compute cards from different vendors. An abstraction layer encapsulates these differences, allowing upper-layer applications to remain agnostic to underlying hardware changes. This requires core frameworks and runtimes to have robust hardware abstraction and portability.

Second, is the logical extension of multi-cloud and hybrid cloud architecture. The most strategic core computing power might reside in self-built data centers, but the architectural design should allow non-core or bursty workloads to run seamlessly on public clouds. Through unified container images and policy-based scheduling, a logically unified but physically distributed "computing grid" can be constructed. Going a step further is the design of an agnostic software stack. From frameworks to model formats, adherence to open standards should be maximized to avoid deep lock-in with any closed ecosystem. This means embracing open frameworks like PyTorch and open model formats like ONNX, ensuring trained model assets can freely migrate and execute across different hardware and software environments. Ultimately, for a strategically resilient computing platform, the core evaluation metric is not just peak performance, but the ability to maintain the continuity of AI R&D and services amidst external environmental shifts. This resilience is a more valuable long-term asset than the performance of any single generation of chips.

From Computing Asset to Intelligent Foundation

The journey of building a ten-thousand-GPU cluster makes it clear that the dimensions of modern AI competition have deepened. It is no longer merely a contest of algorithmic innovation or data scale, but a competition in the ability to transform massive, heterogeneous hardware resources—through immensely complex systems engineering—into stable, efficient, and resilient intelligent services. This process pushes hardware engineering, network science, distributed systems, and software engineering to the forefront of convergence.

Therefore, the value of a ten-thousand-GPU cluster far exceeds the financial asset represented by its staggering procurement cost. It is a nation's or enterprise's core, living intelligent infrastructure for the digital age. Its architecture defines the iteration speed of AI R&D, the scale of service deployment, and the confidence to maintain technological leadership in a turbulent environment. When we examine the computing power race through this systems-engineering lens, we understand that true strategic advantage does not stem from chips stockpiled in warehouses, but from the deliberate, thoughtful technical decisions in the blueprints concerning interconnection, scheduling, and resilience. These decisions ultimately weave cold silicon crystals into the robust foundation supporting an intelligent future.

In late 2025, news of ByteDance's massive investment plan to procure tens of thousands of Nvidia's top-tier AI chips captured the tech world's attention. While media narratives focused on capital rivalry and geopolitics, a far more monumental and complex engineering challenge was quietly overlooked: transforming these chips into usable, efficient, and stable computing power is vastly more difficult than acquiring them. As chip counts scale from hundreds in labs to tens of thousands for industrial use, system design complexity doesn't grow linearly—it undergoes a qualitative leap. The floating-point capability of a single GPU is no longer the bottleneck. How to achieve ultra-high-speed communication between chips, supply massive training datasets at millisecond latency, efficiently distribute and cool immense power loads, and intelligently schedule thousands of computing tasks—these systemic questions constitute an engineering chasm between raw hardware and AI productivity.

This analysis moves beyond capital-driven narratives to delve into the engineering core of building ten-thousand-GPU clusters. The focus is not on which chips companies purchase, but on how these chips are organized, connected, and managed to form an organic whole. From the hardware interconnects within server racks that dictate the performance ceiling, to the software "brain" coordinating everything at data-center scale, to the resilient architecture pre-designed for supply chain uncertainties—this reveals that the core of AI competition has quietly shifted from algorithmic innovation to absolute mastery over foundational infrastructure.

Networking and Storage: The Invisible Performance Ceiling

In a ten-thousand-card cluster, the peak computational power of a single GPU is merely theoretical; its actual output is entirely constrained by the speed at which it receives instructions and data. Therefore, network interconnects and storage systems form the most critical invisible ceiling for the entire system. On the networking front, standard Ethernet is insufficient, necessitating high-bandwidth, low-latency solutions like InfiniBand or dedicated NVLink networks. Engineers face a crucial first decision: choosing the network topology. Should they opt for a traditional fat-tree topology guaranteeing equal bandwidth between any two points, or a more cost-effective but potentially congested Dragonfly+ topology for certain traffic patterns? This choice directly impacts the efficiency of gradient synchronization in large-scale distributed training, thereby dictating model iteration speed.

Parallel to networking is the storage challenge. Training a large language model may require reading datasets ranging from hundreds of terabytes to petabytes. If storage I/O speed cannot keep pace with GPU consumption, the majority of expensive chips will idle in a state of "starvation." Consequently, the storage system must be designed as a distributed parallel file system supported by all-flash arrays, utilizing RDMA technology to enable GPUs to communicate directly with storage nodes—bypassing CPU and OS overhead for direct memory access. Going further, it requires configuring large-scale, high-speed local caches on compute nodes. Using intelligent prefetching algorithms, data anticipated for use is loaded in advance from central storage onto local NVMe drives, forming a three-tier data supply pipeline: "central storage – local cache – GPU memory." This ensures computational units remain continuously saturated. The co-design of network and storage aims to make data flow like blood—with sufficient pressure and speed—continuously nourishing every compute unit.

Scheduling and Orchestration: The Cluster's Software Brain

Hardware forms the cluster's body, while the scheduling and orchestration system is the software brain that gives it intelligence and soul. Once tens of thousands of GPUs and their associated CPU and memory resources are pooled, efficiently, fairly, and reliably distributing thousands of AI training and inference tasks of varying sizes and priorities becomes an immensely complex combinatorial optimization problem. Open-source Kubernetes serves as the foundational layer with its powerful container orchestration, but fine-grained management of heterogeneous resources like GPUs requires extensions such as NVIDIA DGX Cloud Stack or KubeFlow. The scheduler's core algorithm must consider multidimensional constraints: not just GPU count, but also GPU memory size, CPU core count, system memory capacity, and even a task's specific network bandwidth or topology affinity requirements.

A more complex challenge lies in fault tolerance and elastic scaling. In a system composed of tens of thousands of components, hardware failure is the norm, not the exception. The scheduling system must monitor node health in real-time. Upon detecting a GPU error or node failure, it must automatically evict affected tasks from the faulty node, reschedule them on healthy nodes, and resume training from the last checkpoint—all transparently to the user. Simultaneously, facing sudden surges in inference traffic, the system should, based on policy, automatically "preempt" GPU resources from the training task pool to elastically and rapidly scale up inference services, releasing them back once traffic subsides. The intelligence level of this software brain directly determines the cluster's overall utilization rate. This is the critical conversion rate turning massive capital expenditure into effective AI output—a value arguably on par with the performance of the chips themselves.

Resilience and Sustainability: Architecture for Uncertainty

Against a backdrop of technological controls and geopolitical volatility, the architecture of a ten-thousand-GPU cluster must also be infused with the gene of "resilience." This means the infrastructure cannot be designed as a fragile monolith dependent on a single vendor, region, or technology stack. It must possess the ability to continuously evolve and withstand risks under constraints. The first step is pursuing diversification at the hardware level. While pursuing peak performance, the architecture should consider compatibility with compute cards from different vendors. An abstraction layer encapsulates these differences, allowing upper-layer applications to remain agnostic to underlying hardware changes. This requires core frameworks and runtimes to have robust hardware abstraction and portability.

Second, is the logical extension of multi-cloud and hybrid cloud architecture. The most strategic core computing power might reside in self-built data centers, but the architectural design should allow non-core or bursty workloads to run seamlessly on public clouds. Through unified container images and policy-based scheduling, a logically unified but physically distributed "computing grid" can be constructed. Going a step further is the design of an agnostic software stack. From frameworks to model formats, adherence to open standards should be maximized to avoid deep lock-in with any closed ecosystem. This means embracing open frameworks like PyTorch and open model formats like ONNX, ensuring trained model assets can freely migrate and execute across different hardware and software environments. Ultimately, for a strategically resilient computing platform, the core evaluation metric is not just peak performance, but the ability to maintain the continuity of AI R&D and services amidst external environmental shifts. This resilience is a more valuable long-term asset than the performance of any single generation of chips.

From Computing Asset to Intelligent Foundation

The journey of building a ten-thousand-GPU cluster makes it clear that the dimensions of modern AI competition have deepened. It is no longer merely a contest of algorithmic innovation or data scale, but a competition in the ability to transform massive, heterogeneous hardware resources—through immensely complex systems engineering—into stable, efficient, and resilient intelligent services. This process pushes hardware engineering, network science, distributed systems, and software engineering to the forefront of convergence.

Therefore, the value of a ten-thousand-GPU cluster far exceeds the financial asset represented by its staggering procurement cost. It is a nation's or enterprise's core, living intelligent infrastructure for the digital age. Its architecture defines the iteration speed of AI R&D, the scale of service deployment, and the confidence to maintain technological leadership in a turbulent environment. When we examine the computing power race through this systems-engineering lens, we understand that true strategic advantage does not stem from chips stockpiled in warehouses, but from the deliberate, thoughtful technical decisions in the blueprints concerning interconnection, scheduling, and resilience. These decisions ultimately weave cold silicon crystals into the robust foundation supporting an intelligent future.