Huawei Atlas 800T A2 8SFF+2NVMe
- Condition
- New
- Manufacturer
- Huawei
- Form factor
- 4U
- CPU
- 4x Kunpeng 920 (64c/64t, 2.6Ghz, 180W)
- RAM
- 512GB (DDR4 ECC REG)
- NIC card
- 8x 200GE QSFP
- GPU
- 8 x Ascend 910B
- Accelerator manufacturer
- Huawei
- Accelerator model
- Huawei Ascend 910B NPU
- Installed GPU/APU count
- 8 NPUs
- Maximum GPU/APU count
- 8 NPU modules
- Memory per accelerator
- 64GB
- Total accelerator memory
- 512GB
- GPU memory type
- HBM
- Accelerator memory bandwidth
- 800GB/s per NPU
- Accelerator form factor
- Integrated HCCS NPU modules
- Accelerator interconnect
- HCCS high-speed interconnect across the eight-NPU training platform
- Accelerator-to-CPU interface
- Integrated accelerator-module fabric with PCIe 4.0 host I/O
- Supported compute data types
- FP32, FP16 and INT8
- Accelerator virtualization
- Huawei vNPU compute-resource partitioning, device passthrough and container deployment through the Ascend software stack
- MIG support
- Not applicable; NVIDIA MIG is not used on Huawei Ascend accelerators
- Software ecosystem
- Huawei CANN, MindSpore, MindSpeed, AscendCL and the PyTorch Adapter for Ascend
- Maximum system power
- 10.4kW
- Cooling
- Air cooling with 8 hot-swap fan modules and N+1 fan redundancy
- Recommended workloads
- Deep-learning model development and training, LLM pre-training and fine-tuning, distributed training, computer vision, scientific research and high-performance AI computing
- Dimensions
- 175 x 447 x 790 mm (H x W x D)
Need a technical consultation on choosing a server?
Submit a requestHuawei Atlas 800T A2 8SFF+2NVMe is a 4U enterprise AI training server storage configuration with eight 2.5-inch SATA SFF bays plus two 2.5-inch NVMe bays. It is designed for AI laboratories, research institutions, enterprises and service providers that need local capacity and a low-latency tier for model development and distributed training.
Key features:
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Storage basket and service access: The configuration provides eight 2.5-inch SATA SFF bays plus two 2.5-inch NVMe bays. The 8+2 arrangement separates the SATA capacity tier from two NVMe positions for latency-sensitive training data, checkpoints or system services.
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Storage topology: This basket is the official 8 x SATA + 2 x NVMe option. The other supported 7+3 and 4+6 arrangements are separate storage topologies.
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Installed accelerator subsystem: The installed training subsystem contains 8 Ascend 910B accelerators for deep-learning model development and distributed AI workloads.
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Enterprise compute platform: The compute subsystem uses 4 Kunpeng 920 processors and 512GB of DDR4 ECC registered memory for data preparation, orchestration and host-side training services.
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Management, networking and availability: Eight 200GE QSFP ports, RAID support, 4 hot-swap 2.6kW power modules in 2+2 redundancy and N+1 hot-swap fans support distributed training and planned maintenance.
Use cases:
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AI model training and fine-tuning: Uses the installed accelerator subsystem for deep learning, generative AI and large-model development.
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Inference and production AI services: Provides parallel compute and accelerator memory for LLM, multimodal and enterprise inference workloads.
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HPC and scientific computing: Accelerates simulation, numerical analysis and other highly parallel workloads.
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Data-intensive AI pipelines: Combines fast local storage with high-bandwidth compute and network paths for datasets, checkpoints and scratch data.
Huawei Atlas 800T A2 8SFF+2NVMe is a purpose-built storage configuration for organizations that need an 8-SATA plus 2-NVMe basket, 8 installed Ascend 910B accelerators and high-speed networking for distributed AI training.
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