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DeepSeek V4 Flash performance tested: IQ2_S faster than IQ3_XXS-AS

By PulseAugur Editorial · [1 sources] ·

A user conducted performance tests on the DeepSeek V4 Flash model, comparing two quantization levels (IQ3_XXS-AS and IQ2_S) using the fairydreaming/llama.cpp fork. The results showed that the IQ2_S quantization was faster for both initial prompt evaluation and subsequent generation, despite using a smaller context window. The user also found that the fairydreaming fork offered no performance advantage over the mainline llama.cpp build for this model. AI

IMPACT Provides practical performance data for users running DeepSeek V4 Flash on consumer hardware, aiding model selection.

RANK_REASON User-conducted benchmark of an open-source model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

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

DeepSeek V4 Flash performance tested: IQ2_S faster than IQ3_XXS-AS

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0 / 100
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User-conducted benchmark of an open-source model. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release, infra
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High
Clearly on-topic for AI-industry coverage.
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104 days old
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Full methodology in our editorial standards.

COVERAGE [1]

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

    DeepSeek V4 Flash | IQ3_XXS-AS & IQ2_S Bench | mainline b10064 vs fairydreaming | 1xRTX 3090 + 128GB DDR4 | 250PP/11TG on 50K CTX

    <!-- SC_OFF --><div class="md"><p>Hey all!</p> <p>Wanted to see how DeepSeek V4 Flash GGUFs in two different quants perform on my hardware and share the results.</p> <p>Tested two quants on the <strong>fairydreaming/llama.cpp dsv4 fork</strong>. As a bonus, I also ran the same mo…

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