Brand: Intel | Category: GPUs
SKU: HL-225H | Part #: HL-225H | MPN: HL-225H
Contact for Pricing — Request a Quote
The Intel Gaudi 2 HL-225H Mezzanine AI Accelerator delivers supported precision across FP32, BF16, FP16, INT16, and INT8—the three critical AI precision points that define modern model training and inference performance. Built on TSMC 7nm process technology, this mezzanine-form-factor accelerator integrates 24 Tensor Processor Cores and 2 Matrix Multiplication Engines to drive dense compute workloads in OCP-compatible enterprise datacenter servers. The HL-225H pairs 96 GB of HBM2e memory across 6 stacks with industry-leading 2.45 TB/s memory bandwidth, enabling rapid data movement essential for large-scale AI operations.
Networking capability is built into the silicon: 24 integrated 100GbE ports with RoCE v2 deliver 2.4 Tb/s aggregate on-chip network bandwidth (bi-directional), eliminating external interconnect bottlenecks in multi-accelerator topologies. The accelerator connects to host servers via PCIe Gen 4 x16 and operates within a 600 W thermal design power envelope. Intel's SynapseAI software stack provides native support for PyTorch and TensorFlow frameworks, ensuring rapid framework adoption for AI infrastructure teams building next-generation training and inference clusters. The HL-225H (part number HL-225H) runs on Linux operating systems including Ubuntu and CentOS/RHEL.
For IT procurement teams and AI infrastructure architects evaluating accelerator platforms, Intel's Gaudi 2 HL-225H combines on-package memory bandwidth, integrated networking, and multi-framework support in a mezzanine form factor optimized for OCP-standard server deployments. Contact Omnixon Global to request a detailed quotation and technical evaluation for your datacenter requirements.
| Brand | Intel |
| Category | GPUs |
| SKU | HL-225H |
| Part Number | HL-225H |
| Condition | New |
| Manufacturer Part Number | HL-225H |
| Product Family | Intel Gaudi 2 |
| Form Factor | Mezzanine (OCP Accelerator Module) |
| Process Technology | TSMC 7nm |
| Tensor Processor Cores (TPCs) | 24 |
| Matrix Multiplication Engines (MMEs) | 2 |
| Memory Type | HBM2e |
| Memory Capacity | 96 GB |
| Memory Bandwidth | 2.45 TB/s |
| HBM2e Stacks | 6 |
| On-Chip Network Ports | 24 x 100GbE (integrated RoCE v2) |
| Aggregate On-Chip Network Bandwidth | 2.4 Tb/s (bi-directional) |
| Host Interface | PCIe Gen 4 x16 |
| Thermal Design Power (TDP) | 600 W |
| Supported Frameworks | PyTorch, TensorFlow (via Intel SynapseAI SDK) |
| Software Stack | Intel SynapseAI |
| Supported Precision | FP32, BF16, FP16, INT16, INT8 |
| Operating System Support | Linux (Ubuntu, CentOS/RHEL) |
| Target Deployment | OCP-compatible enterprise datacenter servers |
Reference servers include Dell PowerEdge XE9680 / XE9712, HPE Cray XD670, Lenovo ThinkSystem SR685a / SR675 V3, Supermicro AS-A21GE / SYS-821GE, Gigabyte G593 / G894, ASUS ESC. Share your target platform in the RFQ and we will confirm chassis-to-GPU compatibility and recommended NIC pairing.
Highly model-dependent. L40S / RTX-class: typically 3-6 weeks. H100/H200/B200 in SXM form factor: 12-16 weeks for whole-platform allocations. We quote genuine-channel ETAs only — no grey-market promises.
Yes — Omnixon stocks the full NVIDIA networking lineup (Quantum-2 / Quantum-X InfiniBand, Spectrum-X Ethernet, ConnectX NICs, BlueField DPUs) so we can quote a complete training-cluster BOM, not just the GPUs.
Yes. We hold genuine-channels for NVIDIA AI Enterprise software subscriptions. Add it to your RFQ and we quote node-aligned licensing along with the hardware.