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User seeks advice on optimizing hardware for large language models

By PulseAugur Editorial · [1 sources] ·

A user on the r/LocalLLaMA subreddit is seeking advice on optimizing their hardware for running large language models. They are currently using a Qwen3.6 27B model with a 262k context window via llama.cpp on a system with a Ryzen 9 7950X CPU, 128GB of DDR5 RAM, and an RTX 4090/3090 Ti GPU setup. The user notes that their system RAM is not being utilized and they are unable to run larger models like 122B variants, questioning if there are better models available for their hardware. AI

IMPACT Users are exploring ways to maximize their hardware for running increasingly large language models locally.

RANK_REASON User query seeking advice on hardware optimization for LLMs.

Read on r/LocalLLaMA →

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

User seeks advice on optimizing hardware for large language models

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User query seeking advice on hardware optimization for LLMs.
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77 days old
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Common_Warthog_G ·

    I feel like I'm not using my hardware efficiently

    <!-- SC_OFF --><div class="md"><p>Hi there, </p> <p>got a 7950x,128GB DDR5, RTX 4090 and RTX 3090TI.</p> <p>I'm currently running Qwen3.6 27B Q8 with 262k Context at Q8 with llama.cpp. It's not touching the DDR5 RAM at all but at the same time I couldn't get 122B A10B or the like…

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