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Limited VRAM users discuss strategies for running local LLMs

By PulseAugur Editorial · [2 sources] ·

Users with limited VRAM, specifically 8GB or 12GB, are discussing strategies for running local large language models. They are exploring options like smaller fine-tuned models, such as Qwen 3.5 9B or Qwen finetuned MoEs, and debating whether upgrading to 24GB VRAM is necessary for better performance. The conversation highlights the current limitations and desired advancements in model efficiency for consumer hardware. AI

IMPACT Users with limited hardware are seeking efficient models and strategies to run local LLMs, indicating a demand for optimized solutions.

RANK_REASON User discussions on Reddit about hardware limitations for running LLMs.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Limited VRAM users discuss strategies for running local LLMs

COVERAGE [2]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Aggravating-Push-207 ·

    What can us 8 GB VRAM poors do?

    <!-- SC_OFF --><div class="md"><p>I want to hook up a local model to Cline, but it seems the best model is still just Qwen 3.5 9B. <em>Please</em> can we have a Qwen 3.8 9B that gets close to Qwen 3.6 27B?</p> </div><!-- SC_ON --> &#32; submitted by &#32; <a href="https://www.red…

  2. r/LocalLLaMA TIER_1 English(EN) · /u/Mean-Ad1493 ·

    12GB VRAM gang, what's our plan?

    <!-- SC_OFF --><div class="md"><p>Seems like we're limited to qwen finetuned MoEs for now. Looking at the current landscape - focus seems to be on dense models (muse glimmer 30b, qwen 3.8 27b) for smaller setups. </p> <p>Is upgrading to 24GB VRAM the only option?</p> </div><!-- S…

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