Qwen 35B model runs on mobile device at 90 tokens/sec input speed

The researcher began testing Qwen's 35B mixture-of-experts language model—a large language model with 35 billion parameters—on an S26 Ultra mobile device. The core finding is that the active model footprint (the memory required to run the model in practice) fits within the device's available memory. After optimization, the model achieves roughly 90 tokens per second for input processing—the speed at which the model reads and understands incoming text—and approximately 8 tokens per second for output generation, the speed at which it produces responses. No precision loss is reported, meaning the model's accuracy was not sacrificed to achieve these speeds. The researcher notes that the methods and architecture used are not being shared publicly at this stage. The post also includes personal context: the researcher came to AI and machine learning through self-directed study and personal interest rather than formal graduate training, and has invested in computing resources to conduct this kind of testing. The researcher mentions having submitted four papers to arXiv (a preprint repository widely used in machine learning) as first author, but all remain under review; the researcher attributes the hold-up to their lack of institutional affiliation, a barrier that can affect publication timelines in academic contexts. The post closes with an invitation for collaborators interested in expanding the testing effort.

This post represents a case of independent AI research operating outside traditional institutional frameworks. The researcher explicitly notes having learned machine learning based on personal interest rather than through formal PhD study, and has accessed sufficient compute and resources to test a large language model on mobile hardware. The constraint mentioned—that four papers remain on hold on arXiv due to first-authorship status and lack of institutional affiliation—reflects a structural tension in academic publishing: peer review systems often advantage researchers with formal institutional backing, even when the technical work itself may be sound. The call for collaborators suggests the researcher views this as a proof-of-concept worth validating more broadly, though the decision not to share methods or architecture details limits immediate reproducibility.

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