Intel Habana Gaudi 1 HL-205 OAM Training Accelerator

The Intel Habana Gaudi 1 HL-205 is an OAM (OCP Accelerator Module) form-factor deep learning training accelerator built on Habana Labs' first-generation Gaudi architecture. Designed specifically for large-scale AI training workloads in datacenter environments, the HL-205 integrates eight on-die GEMM engines alongside a matrix multiplication unit and a shared SRAM pool to deliver high-throughput tensor computations. The processor incorporates 10 x 100 GbE RoCE v2 ports directly on-chip, enabling scale-out training across multiple nodes without requiring a separate high-speed interconnect fabric, which simplifies cluster topology and reduces infrastructure complexity.

The Gaudi 1 architecture features a heterogeneous compute design combining dedicated Tensor Processing Cores (TPC) — fully programmable VLIW SIMD processors — with the Matrix Multiplication Engine (MME). The TPC cores support a broad range of data types including FP32, BF16, INT16, INT8, UINT8, INT4, and UINT4, providing flexibility for mixed-precision training workflows. The HL-205 carries 32 GB of HBM2 high-bandwidth memory with an aggregate memory bandwidth suited to feeding the high-throughput compute engines during large batch training operations. The OAM mechanical form factor aligns with OCP standards, facilitating integration into OAM-compatible baseboard platforms and high-density server chassis designed for AI infrastructure.

Targeted at enterprises building and operating large neural network training pipelines, the Gaudi 1 HL-205 supports popular deep learning frameworks including TensorFlow and PyTorch through Habana's SynapseAI software suite. The card's native scale-out networking, implemented via on-chip 100 GbE interfaces with RDMA over Converged Ethernet, allows multi-node training clusters to be constructed using standard Ethernet switching infrastructure. This approach is particularly relevant for organizations seeking to scale AI training capacity across UAE, GCC, EMEA, and APAC datacenter footprints without the operational overhead of proprietary interconnect ecosystems.

Ideal for

• Large-scale deep learning model training for natural language processing, computer vision, and recommendation systems in enterprise AI research environments
• Multi-node distributed training cluster deployments leveraging the on-chip 100 GbE RoCE v2 scale-out networking over standard Ethernet fabric
• Mixed-precision AI training pipelines utilizing BF16 and FP32 data types to balance throughput and numerical accuracy for production model development
• High-density AI datacenter buildouts using OAM-compatible baseboards and OCP-aligned server chassis for space- and power-efficient accelerator deployments
• Enterprise MLOps platforms requiring framework-agnostic accelerator support via SynapseAI integration with TensorFlow and PyTorch training workloads
• AI infrastructure modernization projects in hyperscale and cloud-adjacent datacenter environments across EMEA and APAC regions seeking Ethernet-native scale-out training solutions

Technical specifications

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