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LLM Enthusiasts Debate 128GB vs 256GB RAM for Local Inference

By PulseAugur Editorial · [1 sources] ·

A discussion on the r/LocalLLaMA subreddit explores the optimal system RAM capacity when paired with a large amount of VRAM, specifically considering 128GB vs. 256GB. Users are weighing the trade-offs for running various large language models, such as Deepseek v4 and MiMo v2.5, with different quantization levels and VRAM/RAM weight splits. The conversation highlights how model size, context window, and quantization impact RAM requirements for local AI inference. AI

IMPACT Informs hardware choices for individuals running large language models locally.

RANK_REASON User discussion on hardware requirements for running local LLMs.

Read on r/LocalLLaMA →

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

LLM Enthusiasts Debate 128GB vs 256GB RAM for Local Inference

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Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Commentary
User discussion on hardware requirements for running local LLMs.
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High
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Story freshness
49 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Thin_Pollution8843 ·

    128GB vs 256gb of ram

    <!-- SC_OFF --><div class="md"><p>Imagine you have 128gb of VRAM. what accompanying ram capacity you would choose (DDR4 8channel)?</p> <p>For example Deepseek v4 flash in q8 takes around 170GB + 12GB Dflash + ~10GB per 1m context so it’s under 200gb. so 128 + 128 should be good <…

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