The Blueprint for Modern AI Data Centers

As the demand for advanced AI applications continues to grow, data centers must evolve to support massive compute loads, mission-critical workloads, and liquid cooling requirements, all without sacrificing speed, uptime, or flexibility. Supermicro’s Data Center Building Block Solution (DCBBS) is designed to provide everything that you need to outfit a modern AI data center. DCBBS is built on a modular philosophy: complex systems from validated components and sub-systems. From individual GPUs to full racks and facility-side infrastructure, Supermicro enables end-to-end deployment with ultimate flexibility.

Rapid Deployment with First-to-Market

The Data Center Building Block Solution (DCBBS) provides first access to cutting-edge technologies—including the latest GPUs, CPUs, interconnects, storage, networking, and liquid-cooling at any scale— to maximize performance, efficiency, and return on infrastructure investment.

One-stop-shop with Onsite Services

As your single trusted partner for AI infrastructure, Supermicro manages the complete lifecycle from design and assembly to onsite deployment and ongoing support, enabling rapid data center implementation with a monthly capacity of 5,000+ racks—including 2,000+ liquid-cooled racks—available from global production facilities.

Validated for Fast Time to Online

Defined as part of data center building blocks, racks are fully integrated and validated at cluster level with L11 and L12 testing to accelerate time to online and assures plug-and-play deployment.

Customized to Your Workloads

The Building Blocks DNA throughout Supermicro’s solutions allows unbeatable level of customization at server, rack, cluster, cooling, and power level, tailored for your workload and application requirements.

System-Level Building Blocks

Supermicro systems have long been designed with Building Block architecture. This approach is part of why Supermicro is able to offer the industry's widest portfolio of servers which allows for better optimization to the project requirements. Supermicro DCBBS starts here at the system level. It is critically important to carefully fine-tune the system bill of materials (BOM) because it establishes the balance of computing resources for the entire data center. Supermicro offers an unbeatable level of customization, with the freedom to choose individual sub-components.

Modular Design with Endless Customization Possibilities

Fine-tune the balance of data center resources with customization down to the CPUs, GPUs, DIMMs, drives, NICs, and more with various chassis form factors. Multiple system design choices optimize I/O, thermal, power, and cabling to your data center layout.

Broad Range of Systems with Resource Optimization

Building block Solution allows broad range of optimized system designs quickly adopting cutting-edge technologies. Precisely-tailored systems fit the hardware to the application, including specialized systems for scalable AI compute, high-performance storage, and edge computing.

Advanced Cooling Systems

Reduce data center power costs and increase AI per Watt with liquid-cooled CPUs, GPUs, DIMMs, PCIe switches, VRMs, power supplies and more. Optimized airflow by advanced chassis mechanical designs push push limits for compute density and power efficiency.

Reduced Supply Chain Challenges, Fast Production

The common use of modular building block subsystems speeds up time-to-market and removes supply chain bottlenecks. Supermicro’s industry-leading manufacturing capacity with worldwide logistics assures timely assembly and delivery at scale.

Rack & Cluster Building Blocks

Once systems are defined, they’re integrated into rack-level building blocks, the organizational backbone of your cluster.

Optimized cable layouts for reduced airflow obstruction and improved performance
Scalable Units enable 256-node clusters and beyond
Non-blocking network topology for fast node-to-node communication

To achieve the complex building process of large AI clusters, such as 256 system node clusters, it can be made simpler by dividing it into smaller parts. These "scalable units" consist of groups of systems, interconnected with rail-optimized network topology, that can be further multiplied to achieve the desired cluster size.