Intel Habana Gaudi 1 HLS-1 8-OAM Server System

The Intel Habana Gaudi 1 HLS-1 8-OAM Server System is a purpose-built deep learning training platform integrating eight Gaudi 1 OAM (Open Accelerator Module) processors into a high-density 1U server chassis. Based on Intel's Habana Gaudi architecture, each Gaudi 1 processor features 32GB of HBM2 memory and a tightly integrated 100GbE RDMA networking fabric, enabling scale-out training across large clusters without the need for a separate networking switch for single-node configurations. The system delivers substantial aggregate memory bandwidth and compute throughput optimized for the matrix-multiplication-intensive workloads central to modern neural network training.

The eight OAM modules within the HLS-1 system are interconnected via Gaudi's on-chip 100GbE ports, providing a fully non-blocking high-bandwidth interconnect between accelerators within the node. This architecture reduces latency during gradient synchronization phases of distributed training and supports both scale-up within the node and scale-out across multiple HLS-1 nodes. The system interfaces with a host server over PCIe and is managed through Intel's SynapseAI software stack, which provides graph compilation, runtime management, and integration with popular deep learning frameworks including TensorFlow and PyTorch via the Habana plugin ecosystem.

Designed for enterprise datacenter deployment, the Intel Habana Gaudi 1 HLS-1 8-OAM Server System targets organizations running large-scale natural language processing, computer vision, and recommendation model training workloads. Its OAM form factor and open standards alignment reflect the OAM consortium's goals of interoperability and thermal efficiency, making it suitable for hyperscale and enterprise AI infrastructure buildouts across datacenter environments in the UAE, GCC, EMEA, and APAC regions.

Ideal for

• Large-scale deep learning model training for natural language processing tasks including transformer-based architectures such as BERT and GPT variants
• High-throughput computer vision model training across multi-node GPU clusters for autonomous systems and image recognition pipelines
• Recommendation system model training at scale for e-commerce, media streaming, and digital advertising platforms
• Research and development environments requiring rapid iteration on large neural network architectures with high aggregate HBM2 memory capacity
• Enterprise AI infrastructure buildouts seeking an alternative accelerator platform with integrated scale-out networking fabric
• Multi-tenant datacenter AI training services requiring dense accelerator compute in a manageable 1U OAM-compliant form factor

Technical specifications

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