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LLaMA users debate optimal quantization methods for local models

A discussion on the r/LocalLLaMA subreddit explores the current optimal quantization methods for large language models. Users recall that q4 quantization was previously considered the best, offering a balance between performance and VRAM usage, even being adopted by Apple for on-device applications. The thread seeks to determine if newer quantization techniques have since surpassed q4 in efficiency and quality. AI

RANK_REASON User discussion on a subreddit about model quantization, not a primary source release or significant industry event.

Read on r/LocalLLaMA →

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

    Has there been any recent new development on which quant is considered optimal?

    <!-- SC_OFF --><div class="md"><p>I recall in earlier days, q4 was said to be optimal. </p> <p>That is to say, if you have a: </p> <p>small q8 model<br /> medium q4 model<br /> large q2 </p> <p>Assuming they use the same amount of GPU VRAM, medium q4 would be the best-performing …