Orange Pi AI Station Brings 176 TOPS and 96GB RAM to the DIY AI Workbench

176 TOPS in a DIY Form Factor

The Orange Pi AI Station is the most powerful single-board computer the maker community has seen at its price point, and for anyone building a local AI inference workstation, it deserves serious attention. The system is built around the Huawei Ascend 310 SoC — a chip architecture designed specifically for AI acceleration — delivering 176 TOPS of NPU performance across 10 dedicated AI cores.

For context: the Raspberry Pi 5's NPU is measured in fractions of a TOPS. The Orange Pi AI Station is operating in a fundamentally different performance tier.

Specifications That Matter

The Ascend 310 SoC integrates 16 CPU cores running at 1.9 GHz alongside the 10-core NPU and 8 additional vector processing cores optimized for the mathematical operations common in AI inference workloads. This is not a general-purpose processor with a neural accelerator bolted on — the chip architecture is AI-first, with the CPU cores handling orchestration and the NPU handling the heavy compute.

Memory configurations are expansive: buyers choose between 48 GB or 96 GB of LPDDR4X RAM. That upper tier — 96 GB of unified memory — enables running large language models locally that would otherwise require dedicated GPU server hardware. Storage options extend to 256 GB of eMMC on-board, microSD expansion, and a PCIe SSD slot for high-speed additional storage.

Connectivity covers dual Gigabit Ethernet ports and built-in WiFi, making the unit well-suited for both networked inference deployments and standalone local development.

openEuler and the AI Software Stack

The AI Station ships with openEuler 22.03 — a Linux distribution developed by Huawei's open-source community — pre-configured with the CANN (Compute Architecture for Neural Networks) software stack. CANN is Huawei's AI computing framework, providing the necessary runtime and operators for the Ascend 310's inference capabilities.

Developers comfortable with Python and standard AI frameworks will find CANN supports TensorFlow, PyTorch, and ONNX model deployment paths. Video output runs via HDMI at 1080p60, and the full Linux environment makes this a proper development and deployment platform.

Why This Matters for the Self-Hosted AI Space

176 TOPS is the kind of number that enables running 13B and 30B parameter language models at practical inference speeds without GPU rental fees or cloud API costs. For the self-hosted LLM community, the combination of raw AI compute, unified memory headroom, and a hackable Linux platform is a compelling proposition — especially as the mini-computer and SBC market increasingly emphasizes AI workloads.

Orange Pi has positioned the AI Station as a DIY and developer-focused device rather than a turnkey appliance. For technically capable users comfortable with Linux and AI frameworks, that flexibility is a feature, not a limitation. More detailed availability and pricing information is available through Orange Pi's official channels and distributors.

Sources: NotebookCheck (2026), CNX Software (2025), sbc.compare Blog (2026)