Senior Director, Advanced Technologies, Trycay
The rapid acceleration of artificial intelligence workloads is fundamentally reshaping data center architecture. As AI models grow larger and more complex, traditional data center networks—built on a patchwork of specialized interconnect protocols—are reaching their limits. In response, the industry is coalescing around a new architectural vision: the unified fabric.
While this concept promises dramatic gains in performance and efficiency, realizing it requires far more than new protocols. It demands a complete rethinking of the physical interconnect infrastructure that underpins AI-scale computing.
Advances in AI processors have shifted the primary bottleneck in data centers from compute to connectivity. The network fabric—comprising switches, optics, connectors, and cabling that link processors, accelerators, memory, and storage—now plays a decisive role in determining system scalability and performance.
Modern high-performance AI data centers rely on multiple interconnect technologies, including PCIe, NVLink, Ethernet, and the emerging Compute Express Link (CXL). Each protocol is highly optimized for its intended function, yet integrating them into a single system introduces latency, power inefficiencies, and operational complexity. As AI workloads scale, these inefficiencies increasingly constrain overall system performance.
The current approach to AI connectivity is fundamentally heterogeneous. NVLink delivers exceptional bandwidth for GPU-to-GPU communication within a single server, but it does not scale efficiently across nodes. Ethernet and InfiniBand provide cluster-level connectivity, yet their protocol stacks and CPU involvement add software overhead and latency compared to native GPU fabrics. PCIe and CXL enable flexible peripheral and memory expansion, but they were not designed as primary, ultra-high-bandwidth communication fabrics for AI workloads.
As data moves between compute, memory, and storage, it must traverse multiple protocol boundaries. Each transition introduces buffering, translation, and latency penalties that accumulate at scale. The result is underutilized accelerators and slower AI training and inference cycles.
The unified fabric represents a shift from fragmented, domain-specific interconnects to a converged, high-performance network designed explicitly for AI data movement. Its core principle is simplification. Instead of maintaining separate fabrics for compute, memory, and storage, a unified fabric creates a flat, composable architecture that spans the entire data center.
In this model, the data center operates as a single, coherent compute system—often described as a “SuperNode.” GPUs in different racks can access remote memory with minimal overhead, storage traffic flows across the same high-speed fabric, and resources can be dynamically reallocated to maximize utilization.
Industry momentum behind this vision is growing. Vendor-led initiatives such as Huawei’s UB-Mesh aim to deliver more than 10 Tbps per ASIC with sub-microsecond latency, while collaborative efforts like the Ultra Ethernet Consortium are redefining Ethernet for AI-scale workloads. Together, these approaches seek to reduce latency, simplify infrastructure, and improve overall efficiency compared to today’s fragmented designs.
As unified fabric architectures mature at the protocol level, the primary engineering challenge shifts to the physical layer. Moving terabits of data with minimal latency and power consumption places unprecedented demands on the entire interconnect path:
The extreme bandwidth requirements of AI systems are accelerating the adoption of co-packaged optics (CPO), where optical interfaces are integrated close to the processor. This introduces new challenges in thermal management, power delivery, and field serviceability.
Routing 224 Gbps PAM-4 signals across conventional printed circuit boards can severely degrade signal quality, becoming a limiting factor in system performance.
Scaling a unified fabric across thousands of nodes requires ultra-dense, high-speed connectors capable of supporting 1.6 Tbps per port while maintaining signal integrity and thermal stability.
In addition to hardware complexity, unified fabrics must also navigate ecosystem adoption, interoperability, and the need for open, vendor-neutral standards.
Meeting these challenges requires a holistic, end-to-end approach to interconnect design. As an active contributor to the Open Compute Project (OCP), Trycay helps define open standards for next-generation hardware and delivers solutions that address bandwidth density, thermal performance, and reliability across the entire interconnect path.
The Trycay External Laser Source Interconnect System (ELSIS) separates the laser source from the processor package. This pluggable, blind-mating design improves thermal efficiency, enhances serviceability, and increases system safety by eliminating direct user access to optical fibers.
Trycay BiPass technology provides a direct-to-I/O architecture that routes high-speed signals through dedicated low-loss twinax cables, bypassing the PCB. This approach maintains signal integrity at 224 Gbps PAM-4 speeds while reducing the need for power-hungry retimers, lowering both cost and thermal load.
To support 1.6 Tbps and beyond, Trycay offers a portfolio of QSFP-DD and OSFP pluggable connectors. These industry-standard interfaces enable extreme port density while offering advantages such as backward compatibility with QSFP-DD and improved thermal performance with the OSFP form factor.
The transition to a unified fabric marks a fundamental redesign of AI data center connectivity. While protocols and software will continue to evolve, the ultimate performance of any unified fabric is determined by the efficiency, reliability, and scalability of its physical infrastructure.
By applying deep engineering expertise across chip-level, board-level, and rack-scale interconnects, Trycay provides the hardware foundation required to transform the unified fabric from concept to reality—enabling AI data centers to scale with greater performance, efficiency, and resilience.
