Huawei Atlas 900 Pod A2 AI Training Cluster Node
Brand: Huawei | Category: GPUs
SKU: Atlas-900-Pod-A2 | Part #: Atlas-900-Pod-A2 | MPN: Atlas-900-Pod-A2
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About the Huawei Atlas 900 Pod A2 AI Training Cluster Node
The Huawei Atlas 900 Pod A2 AI Training Cluster Node is engineered for large-scale distributed AI training workloads in enterprise datacenters and AI supercomputing environments. This pod-based cluster node integrates Huawei's Ascend 910B NPU processors built on the Da Vinci 2.0 architecture, delivering exceptional parallel training performance across multiple AI frameworks. Built for sustainability in high-density deployments, the system employs liquid cooling to maintain thermal efficiency during continuous operation. AI infrastructure teams deploying mixed-precision training pipelines will find the Atlas-900-Pod-A2 addresses the demanding performance requirements of transformer models, large language models, and computer vision applications at scale.
The cluster node leverages Huawei's ecosystem of native and compatible AI frameworks—MindSpore, PyTorch, and TensorFlow—alongside the Huawei ModelArts cluster management platform with support for YARN and Kubernetes orchestration. Inter-node connectivity spans high-speed optical interconnect with RoCE v2 RDMA over 100GbE and 200GbE fabrics, enabling low-latency synchronization across distributed training jobs. On-node, the HCCS high-bandwidth interconnect fabric ensures coherent communication between multiple Ascend 910B processors. Support for data parallelism, tensor parallelism, pipeline parallelism, and expert parallelism provides architectural flexibility for diverse model training strategies. Omnixon Global delivers the Huawei Atlas 900 Series to customers across UAE, GCC, EMEA, and APAC regions. Contact us for a detailed technical specification sheet and RFQ.
Key Specifications
- AI Processor: Huawei Ascend 910B NPU
- AI Processor Architecture: Da Vinci 2.0
- FP16 AI Compute per Ascend 910B: 320 TFLOPS
- INT8 AI Compute per Ascend 910B: 640 TOPS
- On-chip Memory (HBM2e) per Ascend 910B: 64 GB
- Cluster Interconnect: High-speed optical interconnect with RoCE v2 RDMA over 100GbE / 200GbE fabric
- Cooling System: Liquid cooling
- Cluster Management Platform: Huawei ModelArts; supports YARN and Kubernetes-based scheduling
Technical Specifications
| Brand | Huawei |
| Category | GPUs |
| SKU | Atlas-900-Pod-A2 |
| Part Number | Atlas-900-Pod-A2 |
| Condition | New |
| Product Line | Atlas 900 Series |
| Model | Atlas 900 Pod A2 |
| Manufacturer Part Number | Atlas-900-Pod-A2 |
| AI Processor | Huawei Ascend 910B NPU |
| AI Processor Architecture | Da Vinci 2.0 |
| FP16 AI Compute per Ascend 910B | 320 TFLOPS |
| INT8 AI Compute per Ascend 910B | 640 TOPS |
| On-chip Memory (HBM2e) per Ascend 910B | 64 GB |
| NPU Interconnect Fabric | HCCS (Huawei Cache Coherence System) high-bandwidth on-node interconnect |
| Cluster Interconnect | High-speed optical interconnect with RoCE v2 RDMA over 100GbE / 200GbE fabric |
| Cooling System | Liquid cooling (direct liquid cooling for sustained high-density operation) |
| AI Framework Support | MindSpore (native), PyTorch, TensorFlow (via compatibility layer) |
| Cluster Management Platform | Huawei ModelArts; supports YARN and Kubernetes-based scheduling |
| Parallelism Support | Data parallelism, tensor parallelism, pipeline parallelism, expert parallelism |
| Deployment Environment | Datacenter / AI supercomputing cluster |
| Form Factor | Pod-based cluster node (rack-integrated density module) |
| Target Regions | UAE, GCC, EMEA, APAC |
Frequently Asked Questions about Huawei Atlas 900 Pod A2 AI Training Cluster Node
What server platforms accept the Huawei Atlas 900 Pod A2 AI Training Cluster Node?
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.
How long is the lead time on AI GPUs?
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.
Do you supply matched networking (Quantum InfiniBand / Spectrum-X)?
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.
Can you help with NVIDIA AI Enterprise licensing?
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.