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Qwen 35B MoE LLM successfully tested on mobile device

By PulseAugur Editorial · [1 sources] ·

An individual has successfully tested a private Qwen 35B MoE LLM runtime on a Samsung S26 Ultra, finding that the model's active footprint fits within the device's memory. Initial optimizations yielded approximately 90 input processing tokens per second and 8 output tokens per second on the mobile device. The individual, who is self-taught in AI/ML and lacks formal institutional affiliation, is seeking collaborators for further testing and has had papers held up on arXiv due to their independent status. AI

IMPACT Demonstrates the potential for running sophisticated LLMs on mobile hardware, suggesting future advancements in on-device AI capabilities.

RANK_REASON The item describes a technical test and performance evaluation of a specific LLM on a consumer device, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Qwen 35B MoE LLM successfully tested on mobile device

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0 / 100
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The item describes a technical test and performance evaluation of a specific LLM on a consumer device, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release, product
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High
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44 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Severe_Post_2751 ·

    Tried testing qwen 35b moe model on s26 ultra , without compromising on precision [R] ,[D]

    <!-- SC_OFF --><div class="md"><p>Started testing a private qwen 35B moe capacity LLM runtime on s26 ultra, early testing shows that active model footprint can fit within the device’s memory limits.( not sharing the methods or architecture used) and results suggest roughly 90 inp…

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