AI workloads move enormous volumes of data between GPUs, memory, and storage. Modern infrastructure must support continuous high-bandwidth communication across thousands of compute nodes.
Efficient AI systems depend on fast coordination between processors. Even small increases in latency can reduce cluster efficiency and slow large-scale training or inference workloads.
Modern AI networks operate at hundreds of gigabits per second per link. Maintaining signal fidelity at these speeds requires advanced signal-integrity technologies across both optical and copper channels.
AI workloads generate massive internal data flows between servers, GPUs, and storage within the data center. Scalable interconnect fabrics are essential to prevent congestion and maintain performance.
As AI models grow larger and more sophisticated, the infrastructure supporting them must move and process data at unprecedented speed and scale.
Leading AI deployments now span tens of thousands, and increasingly hundreds of thousands of GPUs. These environments demand connectivity technologies that scale reliably while maintaining performance and efficiency.
Modern AI training and inference rely on massive GPU clusters operating in parallel. These systems move extraordinary volumes of data continuously between GPUs, memory, and storage.
High-speed connectivity keeps these clusters synchronized. Ultra-low-latency interconnects allow thousands of GPUs to operate as a unified computing platform, accelerating training cycles and enabling AI systems to scale to tens of thousands of accelerators and beyond.
Hyperscale data centers form the backbone of modern AI and cloud computing. These facilities must sustain enormous bandwidth while maintaining reliability and efficiency across thousands of interconnected systems.
High-performance optical and copper interconnects provide the communication fabric linking compute, storage, and networking layers, allowing data to move efficiently across increasingly large and complex infrastructure.
AI infrastructure depends on hierarchical network fabrics built around both scale-up and scale-out connectivity.
Scale-up networks tightly connect GPUs within servers and racks to create high-performance compute clusters. Scale-out fabrics extend these connections across racks and data center networks, enabling AI workloads to scale across thousands of nodes.
Maintaining signal integrity across these fabrics is essential to sustaining performance as data rates increase and system architectures grow more complex.
Modern AI systems depend on high-performance interconnects within servers and across data center networks. Technologies such as PCIe and active copper interconnects enable low-latency, high-bandwidth communication between GPUs, CPUs, memory, and accelerators.
As data rates increase, maintaining signal integrity across electrical and optical links becomes increasingly complex. Litrinium develops technologies that enable reliable communication across these interconnects, supporting the next generation of PCIe, active copper connectivity, and AI system architectures.
High-speed connectivity technologies developed for data center infrastructure also extend to networks beyond the data center. Applications such as 5G/6G, broadband access, high-capacity transport, and emerging global connectivity platforms across terrestrial and non-terrestrial networks require reliable data transmission at increasingly higher speeds.
Maintaining signal integrity across these networks enables scalable, high-performance connectivity across modern global infrastructure.