Mistral Large 3

105 comments collected (top 25 by upvotes visible), dominated by Reddit local-LLM threads and Lemmy. The single most substantive hands-on datapoint is a Strix Halo owner running 'mistral-medium-3.5-128b-q4' via llama.cpp (build 8967, commit fc2b0053): prompt processing hits 46.70 t/s but generation only 3.26-3.30 t/s — painful interactive speed even on dedicated local hardware. Community framing of the dense 128B release is skeptical-amused: '128B dense is an interesting niche' (+159), and the top-adjacent joke 'a you can run it locally, but you won't like the experience niche' (+125), though that same user hopes it beats same-size models better than Mistral Small 4 did. Threads derail into 'who is the densest now' debates (Qwen 27B, Gemma 31B/124B, Llama 3.1 405B, a 1T franken-merge), plus waiting for a Qwen 3.6 122B. One Lemmy user reports Mistral models running 'fine' in LM Studio on an 8GB MacBook Air and M2 Max — but cites 3GB downloads, i.e., the small models, not Large 3. Critically, a large share of Lemmy comments are generic AI ethics/skepticism (stolen training data, Kenyan annotation workers, environmental cost, hallucination-prone output in professional domains, a programmer who phased LLMs out of his workflow) with zero Mistral-specific content — high volume, low product signal.

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