I managed to get hands-on with two engineering prototypes that demonstrate exactly how this silicon translates into a real-world AI workflow.
The Fan-Assisted Foldable: 300+ tokens per second on a phone #
The first prototype is a foldable device powered by the Xuanjie O100. It looks like a standard foldable, but the back is a heavy-duty metal chassis featuring a circular intake/exhaust vent with an active cooling fan built right in.
The goal here wasn't just to make a phone, but to create a mobile AI powerhouse. Running the Xiaomi MiMo 3B model entirely offline (no Wi-Fi, no cellular), the responsiveness is staggering. Using the XRING LAB testing tool, I clocked a Time to First Token (TTFT) of roughly 0.45 seconds.
The generation speed is where it gets wild:
Average Generation Speed:~303 tokens/s** Peak Generation Speed:**Up to 330 tokens/s
At these speeds, the AI is literally writing faster than you can read. For anyone interested in prompt engineering or real-time translation, this is the holy grail. You get instant, "zero-latency" interaction for document polishing or live transcription, all while maintaining absolute data privacy because the processing happens strictly on-device.
The AI Cube: Running a 120B model on your desk #
If the foldable is about mobility, the "Xiaomi AI Cube" is about brute-force local compute. This thing is a CNC-machined piece of aerospace aluminum, designed to dissipate massive amounts of heat through 33,874 precision-cut cooling holes. This isn't your typical mini-PC. It’s an Android-based AI terminal with 80 GB of RAM (supporting up to 160 GB) specifically built to deploy 120B parameter models locally. During the demo, we tested its coding capabilities:
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Task Input: A request to generate a functional piano web application.
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Processing: The system utilized a dual-model switching strategy (3B + 120B).
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Output: The 120B model handled the heavy lifting of logic and code structure.
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Result: Within moments, a fully functional "Web Audio Online Piano" was running on the screen, complete with real-time audio waveforms and interactive keys.
The "small model + large model" workflow is the clever part of this deployment. The 3B model handles high-frequency, lightweight commands instantly so the UI feels snappy, while the 120B model is summoned only when the task requires deep reasoning or complex coding. It effectively turns a desktop tool into a local LLM agent that doesn't need a cloud connection to function.
Hardware-driven AI differentiation #
Seeing these two prototypes side-by-side clarifies the future of the AI workflow. We are moving toward a tiered compute model:
Mobile/Foldable Tier: Focused on high memory bandwidth (thanks to the O100's 1.22 TB/s bandwidth) to enable instant, offline interaction for daily tasks.Desktop/Cube Tier: Focused on massive parameter counts and sustained thermal performance to handle heavy-duty reasoning and development.
The bottleneck for local AI has always been the trade-off between model intelligence and latency. By optimizing the silicon specifically for these workloads, Xiaomi is showing that we might finally be able to move away from cloud-dependent LLMs toward a truly private, local AI ecosystem.
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