Houston firm designs, builds, and operates RoCEv2, InfiniBand, and Ultra Ethernet GPU fabrics, NVMe-oF storage, and liquid-cooled GPU pods for enterprise IT
Innovorbs Technologies, a global managed service provider (MSP) and IT solutions company, today announced an AI Data Center Infrastructure practice for enterprises and IT service firms building private AI capacity on-premises or in colocation. The practice covers the full stack beneath a GPU cluster: lossless back-end fabrics, front-end and storage networks, compute and cooling, and the automation and observability needed to run training and inference workloads at scale.
An AI cluster is only as fast as its slowest link. We engineer the fabric, the storage path, and the cooling as one system so the GPUs actually run at the speed the client paid for.”
— Tayyab Muhammad, CEO & Founder, Innovorbs Technologies
AI workloads behave differently from enterprise traffic, and the practice is built around the protocols that make them work. For the GPU-to-GPU back-end fabric, Innovorbs designs RDMA over Converged Ethernet (RoCEv2) on 400G and 800G Ethernet with Priority Flow Control (PFC), Explicit Congestion Notification (ECN) and DCQCN congestion control, adaptive routing, and rail-optimized non-blocking Clos topologies on NVIDIA Spectrum-X, Arista, Cisco Nexus, and Juniper QFX platforms; NVIDIA Quantum InfiniBand with NDR and XDR for clusters that require it; and Ultra Ethernet Consortium (UEC) transport as it reaches production. Collective communication is validated end to end with NCCL and GPUDirect RDMA, and intra-node NVLink and NVSwitch domains are mapped into the fabric design.
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The front-end network uses BGP EVPN-VXLAN with eBGP underlays, while storage traffic runs on NVMe over Fabrics (NVMe/TCP and NVMe/RoCE), GPUDirect Storage, and parallel file systems such as Lustre, WEKA, and VAST. Compute layers cover NVIDIA HGX and DGX, AMD Instinct, and Dell, Supermicro, and HPE GPU servers with Kubernetes, Slurm, and NVIDIA Base Command orchestration. Facilities engineering addresses 40 to 130 kW per rack with direct-to-chip liquid cooling and rear-door heat exchangers. Every build includes telemetry streaming into the company’s Autonomous NOC for congestion, link flap, and GPU health detection with automated remediation.
Delivery is led by CCDE architects with CCIE Data Center and JNCIE-DC engineers, dual-VCIX virtualization specialists, and NVIDIA-certified network professionals, available as fixed-scope design-build projects, 24/7 managed operations, or white-label engineering for IT service firms and colocation providers.
“An AI cluster is only as fast as its slowest link, and most enterprise networks were never built for lossless traffic. We engineer the fabric, the storage path, and the cooling as one system so the GPUs actually run at the speed the client paid for,” said Tayyab Muhammad, CEO and founder of Innovorbs Technologies.
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