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Cyanogen co-founder unveils Kora, a Linux OS that splits AI models across the CPU, GPU, and NPU
Intel NPU support is planned, but founder says AMD’s NPU likely won’t work for language models.
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A team led by Cyanogen co-founder Kirt McMaster has unveiled Kora, a new Linux desktop OS with integrated AI models and agents that can split workloads across CPU, GPU, and NPU resources, as well as other machines on the same local network. We reached out to the firm for more info.
Kora, the first product from Kora Systems Inc., previously Playtron, carries the tagline “The Operating System for Intelligence” and has its own Cortex “hybrid inference engine” technology, which is not open source and is optimized for local models. The OS, built “from the kernel to the shell,” runs local models by default and cloud models when needed, and is expected to arrive on PCs in 2027.
The desktop follows the AI-focused motif with “neural workspaces” and a “neural file system,” which refer to sandboxed workspaces and files indexed by semantic meaning. Kora has three operating modes: “Work," "Create," and "Play." That last base is covered by GameFrame, which descends from Playtron’s GameOS. Humain OS, which Kora is developing with Saudi AI company Humain, is the enterprise variant of this OS.
Currently, the only NPU named in the release material is the Hexagon NPU found in the Snapdragon X2 Elite, which Qualcomm pegs at 80 TOPS. On the X2 Elite, Cortex can schedule AI workloads across the CPU, GPU, and NPU, as well as other systems on the local network, which could be a novel capability if it works beyond the Qualcomm hardware.
McMaster told Tom’s Hardware that the Snapdragon X2 Elite NPU runs Google's Gemma 4 26B model at 25 tokens per second for decode. Intel and AMD NPU support is in the works, but according to the developer, AMD’s NPU will likely be limited to speech and embedding models, not LMs.
Each NPU vendor has its own software stack, or runtime, and prefers that models are compiled in its own format: Qualcomm with QAIRT/QNN, Intel with OpenVINO, and AMD with Ryzen AI software. The press kit doesn't identify support for any Intel or AMD chip, NPU runtime, model format, quantization, or speed figures for those platforms.
Our earlier Windows testing showed the same kind of divide: Snapdragon reached its NPU through the QNN provider under ONNX Runtime, while Intel’s work ran through IPEX, its extension for PyTorch, on the GPU because it was faster than the NPU.
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