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Older LLM quantization format outperforms newer one on Apple M2

By PulseAugur Editorial · [1 sources] ·

A recent test comparing two local Large Language Models (LLMs) on an Apple M2 laptop revealed that the older Q4_K_M quantization format outperformed the newer MXFP4 format. The Q4_K_M format achieved 4.7 tokens/second, completing a 200-token generation in 44 seconds, while MXFP4 managed only 2.6 tokens/second, taking 71 seconds for the same task. The author theorizes that MXFP4's performance on the M2 chip is hindered by dequantization costs and its reliance on hardware features not fully supported by the M2, unlike newer chips such as the M4. AI

IMPACT This finding suggests that older quantization formats may still be preferable for performance on certain hardware, contrary to marketing claims for newer formats.

RANK_REASON The item details a comparative performance test of different LLM quantization formats on specific hardware, presenting empirical results and analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Older LLM quantization format outperforms newer one on Apple M2

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34 / 100
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The item details a comparative performance test of different LLM quantization formats on specific hardware, presenting empirical results and analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Pitambar Mahato ·

    I Tested Q4_K_M vs MXFP4 on the Same Laptop — The Supposedly-Faster New Format Lost

    <p>I tested two local LLMs in two different quantization formats on the same laptop, on the same prompt, three trials each. The result is the opposite of what the marketing says: the supposedly-faster new format lost by 1.8x.</p> <p>Q4_K_M (the older integer-based format) hit <st…

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