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ExLlamaV3 shows significant speed gains over llama.cpp for Qwen model

A user on Reddit's r/LocalLLaMA community shared their experience with ExLlamaV3, reporting significantly faster performance compared to llama.cpp when running the Qwen-3.8-Flash-Next model. The user observed a 3.2x increase in prefill speed and a 2x improvement in decode speed, along with better output quality. However, they noted that this performance advantage is model-dependent, as ExLlamaV3 ran slower than llama.cpp on a different model, the GLM 5.3 Flash, on their setup. AI

IMPACT ExLlamaV3 offers a potential performance upgrade for local LLM inference, particularly for specific models like Qwen.

RANK_REASON User benchmark comparing two inference engines for local LLM deployment.

Read on r/LocalLLaMA →

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

ExLlamaV3 shows significant speed gains over llama.cpp for Qwen model

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User benchmark comparing two inference engines for local LLM deployment.
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infra, product
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High
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19 days old
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Full methodology in our editorial standards.

COVERAGE [1]

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

    exllamav3 comfortably beats llama.cpp running CPU-offloaded Qwen-3.8-Flash-Next on my setup!

    <!-- SC_OFF --><div class="md"><p>I got 2x 20GB RTX 3080s + 128GB of DDR4 2666hz RAM (only 4 of 6 channels populated) + a Xeon 6148 </p> <p>I've always been a llama.cpp person and I've been running <strong>Unsloth's Q4_K_XL</strong> quant of Qwen 3.8 Flash Next at <strong>~270tps…