Wiwynn Intel Gaudi 3 8-OAM AI Training Server SV7-G3

The Wiwynn SV7-G3 is a purpose-built 2U AI training server integrating eight Intel Gaudi 3 OAM (Open Accelerator Module) processors, delivering a tightly coupled, high-bandwidth compute platform engineered for large-scale deep learning training and inference workloads. Each Intel Gaudi 3 accelerator features 128 GB HBM2e memory per OAM module, providing the server with an aggregate of 1024 GB of high-bandwidth accelerator memory across the eight modules. The Gaudi 3 architecture incorporates 24 fully integrated 200 Gb/s RDMA-capable Ethernet ports per OAM for scale-out networking, enabling direct server-to-server fabric communication without external network adapters and reducing interconnect bottlenecks common in large AI clusters.

The SV7-G3 chassis is designed around OAM form factor compliance, supporting the OAM Universal Baseboard specification to ensure thermal, mechanical, and electrical compatibility with Intel's Gaudi 3 module generation. The server accommodates dual Intel Xeon Scalable host processors and high-capacity system DRAM, providing sufficient CPU headroom for data preprocessing pipelines running concurrently with GPU-side training jobs. High-speed PCIe Gen 5 connectivity links the host CPU complex to the OAM baseboard, and the platform supports NVMe storage expansion for local dataset staging. The system is optimized for air-cooled datacenter deployments and is compatible with industry-standard 19-inch rack infrastructure.

Targeted at enterprise datacenters, cloud service providers, and sovereign AI infrastructure programs across the UAE, GCC, EMEA, and APAC regions, the SV7-G3 is positioned as a dense, scalable node for multi-node LLM training, generative AI development, and high-throughput AI inference serving. The platform leverages Intel's open software ecosystem including the Intel Gaudi software suite, Habana SynapseAI SDK, and compatibility with PyTorch and TensorFlow frameworks, enabling organizations to build on open standards rather than proprietary software lock-in.

Ideal for

• Large language model (LLM) pre-training and fine-tuning across multi-node GPU clusters using scale-out RDMA Ethernet fabric
• Generative AI model development including diffusion models and transformer-based architectures requiring high aggregate HBM capacity
• Enterprise AI inference serving for high-throughput, low-latency deployment of production-scale foundation models
• Computer vision and multimodal AI training workloads in healthcare, manufacturing, and autonomous systems research
• Sovereign AI datacenter buildouts in GCC and EMEA regions requiring open, auditable software stacks and OAM-compliant hardware
• HPC and scientific computing workloads that benefit from high-bandwidth memory and integrated RDMA networking at scale

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.