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Users explore combined memory setups for running LLMs locally

By PulseAugur Editorial · [1 sources] ·

A user on Reddit's r/LocalLLaMA subreddit is seeking advice on optimizing the performance of large language models by combining different memory types. They are asking if it's feasible to use a setup that includes 16 GB of VRAM, 64 GB of RAM, and SSD storage for offloading model components. The user has attempted to run models using llama.cpp with specific configurations but is experiencing very low performance, achieving only 6 tokens per second, which they deem unusable. AI

IMPACT Users are exploring ways to optimize local LLM inference hardware configurations.

RANK_REASON User query seeking technical advice on hardware configuration for running LLMs.

Read on r/LocalLLaMA →

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

Users explore combined memory setups for running LLMs locally

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1 / 100
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Commentary
User query seeking technical advice on hardware configuration for running LLMs.
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Breaking (< 6h)
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Additional-Ordinary2 ·

    Is it possible to run it with a combined memory setup: 16 GB VRAM + 64 GB RAM + SSD for offloading n-grams?

    <!-- SC_OFF --><div class="md"><p>Hardware: rtx 5080 16 gb vram; 64 gb ram ddr5 6000hz; ssd with unlimited memory; ryzen 7 9800 x3d.<br /> OS: Windows 11<br /> Software: I’d prefer llama.cpp, but it’s not a strict requirement; I’ll use whatever you suggest, as long as it works on…

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