Maximize GPU utilization
Boost utilization by offloading latency-sensitive communication work so GPUs spend more time on model compute and less time sitting idle. This reduces latency and increases bandwidth, improving end-to-end performance.
Predictable AI economics at scale
NR2 reduces network stalls and jitter, helping AI factory operators scale without sacrificing responsiveness or cost predictability. The result is more throughput per dollar from higher accelerator utilization.
Lower energy use with efficient inference
By reducing wasted compute time and unnecessary host overhead, NeuReality’s AI-SuperNIC improves system-level efficiency and supports better performance per watt as clusters grow.
NR2 AI-SuperNIC for predictable GPU scale-out
NeuReality’s NR2 product follows a modular roadmap that begins with the NR2 AI-SuperNIC and expands the system’s control and orchestration path with Arm Neoverse-based compute. The solution helps hyperscalers, semiconductor companies, and enterprises build best-in-class AI factories with any GPU or XPU, unlocking higher accelerator utilization, more predictable performance, and distributed inference that scales with demand.
The NR2 AI-SuperNIC is a purpose-built network solution for GPU scale-out in large AI training and inference clusters, delivering high-performance distributed AI networking with predictable low latency and high bandwidth across servers and racks.
Enabling AI infrastructure on Arm
NR2 integrates into modern platforms as a standalone AI-SuperNIC or as part of an integrated solution with Arm Neoverse CSS V3-based compute.
NeuReality simplifies AI inference deployment with a cohesive, production-ready system that increases bandwidth, reduces latency, and minimizes jitter across distributed workloads. Its AI-Hypervisor and control software manage distributed inference end-to-end, enabling efficient deployment, scaling, and consistent performance. Built as an integrated platform, NeuReality combines efficient compute, purpose-built networking, and a unified Inference Serving Stack to harness heterogeneous resources across the node and bring AI systems to production faster without requiring teams to manage hardware-specific complexity.