The Data Revolution in AI Factories: Driving High-Speed Networking Forward

Index

High-Speed Connectivity: The Lifeline of AI Infrastructure

From Server Interconnects to Network Topology: Make AI Data Flow Better

GIGABYTE’s Integrated Solutions: Delivering End-to-End AI Infrastructure Services

Shaping the Future of AI Infrastructure with Performance and Sustainability

As AI infrastructure advances, the importance of data transmission has grown dramatically, with computing workloads shifting from CPUs to GPUs. Training large-scale AI models often requires thousands of GPUs to process terabytes to petabytes of data, far beyond the capacity of a single server. This makes large-scale, cross-node collaboration essential, and achieving high bandwidth and low latency networking becomes a critical requirement.

During the AI training phase, multiple GPUs must frequently synchronize and exchange data to keep model parameters consistent. Any delay at a single node can degrade overall efficiency or leave GPU resources idle. This is why stable and fast east-west data transmission is indispensable. In the inference phase, the flow of data shifts toward north-south traffic, moving between the data center and external users. The priority here is real-time responsiveness and service stability, ensuring that every request is processed promptly and reliably.

As AI factories scale up, achieving efficient, highly synchronized, and scalable infrastructure requires more than raw computing power - it demands advanced high-speed interconnects and optimized network topologies tailored to different traffic patterns and application stages. To understand this evolution, let’s break it down across three layers: internal server connections, cross-node interconnects, and data center-wide network architecture.

*Layer 1. Internal Server Transmission: Accelerating CPU-GPU Collaboration
AI workloads involve massive data exchange between CPUs, GPUs, and memory. If internal transmission suffers from latency or insufficient bandwidth, overall AI performance drops significantly. To address this, the industry has introduced multiple technologies to enhance internal interconnects:

 -CXL (Compute Express Link): Built on PCIe Gen5, CXL allows CPUs and accelerators such as GPUs and FPGAs to share memory, reducing redundant data movement and replication. GIGABYTE high-performance servers leverage PCIe Gen5 with CXL technology to dramatically boost CPU-GPU collaboration, optimizing real-time inference and large-scale analytics. 
Further reading:

*Layer 2. Cross-Node Network Architecture: Building High-Speed, Low-Latency AI Training Clusters 
When AI models grow so large that they must be distributed across multiple servers, the efficiency of cross-node data exchange becomes a critical factor for overall training performance. To achieve this, mainstream architectures rely on Ethernet and InfiniBand, along with the crucial Remote Direct Memory Access (RDMA) technology. RDMA allows data to be transmitted directly from the memory of one server to another, bypassing CPU involvement. Think of it like a courier delivering straight to the recipient without waiting at the front desk. This dramatically accelerates data transfer and reduces latency.

- Ethernet: The most widely adopted standard in data centers, Ethernet is known for its maturity and interoperability. To meet the high-speed, low-latency demands of AI workloads, Ethernet can leverage the RoCE (RDMA over Converged Ethernet) protocol to enable RDMA, allowing inter-server data transfers to bypass the CPU, reducing latency and improving efficiency. It also helps minimize packet loss under heavy loads, preventing training interruptions and resource waste. Today’s Ethernet standards support up to 400 Gbps, with 800 Gbps on the horizon, positioning Ethernet as a core component of next-generation AI infrastructure. GIGABYTE’s Intel® Gaudi® 3 Platform Server Solutions adopt an open Ethernet architecture, delivering cost-effective, scalable solutions for AI deployment.

- InfiniBand: Purpose-built for high-performance computing (HPC), InfiniBand delivers ultra-low latency and exceptionally high bandwidth, making it ideal for massive GPU synchronization and large-scale AI model training. With built-in RDMA capabilities, it accelerates data transfers while reducing system loading. InfiniBand currently can go up to 400 Gbps, and the industry is advancing toward 800 Gbps and beyond, securing its position as a key technology for AI supercomputers and hyperscale cloud data centers.

*Layer 3. Data Center Network Topology: Moving Beyond Traditional Three-Tier, Embracing Fat-Tree Design

Network topology is like the traffic map of a data center, defining the pathways for data exchange between servers. It directly impacts the speed and scalability of AI training. In the past, data centers primarily handled north-south traffic, which is the communications between users and servers. To support this, the traditional network design relied on a three-tier architecture: first tier of Access Layer connecting servers, second tier of Distribution Layer aggregating traffic, and final tier of Core Layer for high-speed forwarding. This structure worked well for conventional applications, where most traffic flowed vertically between users and applications. However, AI training changes everything.

In AI and high-performance computing (HPC) environments, the dominant pattern is east-west traffic of massive data exchanged among thousands of servers, particularly GPU servers. Under the traditional three-tier design, this traffic must travel up through the core layer and back down again to reach another server. The result: longer paths, single points of congestion, and bottlenecks at the core. It’s like forcing every car on a highway to pass through the same toll booth, causing severe delays and slowing the entire process.

To overcome this limitation, modern AI and HPC data centers are adopting Fat-Tree topology, built on a spine-and-leaf architecture. Instead of a single highway, Fat-Tree creates a mesh of interconnected pathways, ensuring equal-distance connections between any two servers while distributing traffic to prevent congestion at a single node. This design delivers higher bandwidth, lower latency, and greater reliability, making it ideal for the large-scale data exchanges that AI training demands.

GIGAPOD is an integrated solution purpose-built for AI data centers. A single air-cooled configuration can consolidate 256 GPUs across 8+1 racks. At its core, GIGAPOD adopts a non-blocking Fat-Tree topology, structuring racks using a spine-and-leaf concept to maximize bandwidth and balance traffic.

Here’s how it works: in GIGAPOD, each GPU in a server is paired with an NIC card, creating 8 GPU-NIC pairs per server. Each GPU-NIC pair in a server is connected to a different leaf switch in the middle layer. Next, the leaf and spine switches are connected to form a fat tree. This expansion to the top layer follows a similar concept to connecting servers to leaf switches. Ports from each leaf switch are evenly distributed among spine switches, forming a top layer network. This design enables high-bandwidth, low-latency connectivity, supporting massive horizontal scalability for AI workloads. Even when training the largest models, the cluster maintains efficiency and stability.

The performance of an AI training cluster no longer depends solely on GPU count and compute power, it hinges on the efficiency of data exchange between GPUs and across nodes. This requires a holistic approach that considers interconnected architecture, network topology, and communication protocols to build a high-speed, stable, and scalable infrastructure. Network design is far more than stacking switches and cables; it involves meticulous planning of cabling routes, switch rack placement, cable length optimization, and seamless integration with cooling and power systems.

Building a truly AI-ready data center demands more than isolated technologies. It requires an end-to-end, one-stop service that spans planning, design, construction, and deployment, ensuring perfect alignment between hardware, software, and foundational infrastructure for maximum performance. Drawing on the success of integrating data center deployment for global customers, GIGABYTE delivers Level 12 data center services for large-scale AI data centers worldwide, and continues to provide comprehensive, reliable AI infrastructure solutions.

GIGABYTE offers full lifecycle data center services, from scalable infrastructure design to global technical support. This includes consulting, site and environment planning, engineering and construction, and system deployment. Our solutions integrate the proprietary GPM (GIGABYTE POD Manager) intelligent management platform to streamline infrastructure management and AIOps, creating a fully integrated, end-to-end experience. This one-stop service model simplifies deployment, acelerates time-to-value, and helps enterprises confidently advance toward the future of AI infrastructure. 
Further Reading: 《Data Center Infrastructure》 

As energy consumption and thermal challenges become increasingly critical, GIGABYTE is committed to driving green, sustainable development by implementing high-efficiency cooling technologies such as liquid and immersion cooling, helping enterprises achieve their carbon-neutral goals. At the same time, to address the growing complexity of AI workloads, GIGABYTE continues to enhance its intelligent management platform, GIGABYTE POD Manager, by integrating DCIM (Data Center Infrastructure Management) and AIOps (AI for IT Operations) capabilities. These enable real-time monitoring, automated resource allocation, and predictive maintenance, further improving compute efficiency while reducing operational costs. 
Futher Reading: 《DCIM x AIOps: The Next Big Trend Reshaping AI Software》

Leveraging extensive practical experience and deep technical expertise, GIGABYTE partners with customers to build future-ready AI infrastructures with long-term competitiveness. By strengthening collaboration with ecosystem partners, we deliver innovative, efficient, and sustainable AI data center solutions, creating a smart and resilient data center ecosystem. Through complete hardware-software integration, we continue to accelerate the advancement of AI, paving the way for a smarter, more efficient, and more sustainable future.

Thank you for reading this article. For further consultation on how you can incorporate GIGABYTE hardware and software in your data center, or to build an efficient and optimized AI Infrastructure, we welcome you to reach out to our representatives. Contact us.