The AI Networking & DPUs category covers data processing units (DPUs), SmartNICs, and AI-accelerated network adapters designed to offload packet processing, security, storage virtualization, and telemetry workloads from the host CPU. Products here typically integrate programmable Arm cores, hardware accelerators, and high-speed network ports (25GbE through 400GbE) on a single PCIe card, enabling the host processor to dedicate its cycles entirely to application or inference workloads. Key buying considerations include PCIe generation and lane count, supported offload engines (RDMA/RoCE, NVMe-oF, IPsec/TLS, Open vSwitch), on-board memory capacity, SDK and DOCA/P4 programmability, and thermal envelope for dense 1U/2U server slots.

These adapters are used in GPU inference clusters where CPU overhead from networking would otherwise bottleneck throughput, in hyperconverged and software-defined storage deployments requiring NVMe-oF target offload, and in zero-trust datacenter architectures that push microsegmentation and encrypted tunneling onto the NIC rather than the host. Typical buyers include datacenter architects sizing AI training fabric, cloud service providers building tenant isolation, and enterprise storage teams deploying disaggregated NVMe pools.