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DFlash2 shows speed gains but increased memory use for Qwen3.8 27B

A user tested DFlash2, a new method for accelerating large language model inference, with the Qwen3.8 27B model on an RTX 5090 GPU. While DFlash2 showed speed improvements, particularly for code generation, reaching up to 200 tokens/second in short bursts, it also proved to be more memory-intensive than previous methods like MTP. The user noted that the increased memory usage limited the context window size, potentially making it less practical for their specific use case despite the performance gains. AI

IMPACT This inference optimization technique shows promise for faster LLM generation, though memory constraints may limit its immediate widespread adoption.

RANK_REASON User testing of an inference optimization technique on a specific model and hardware.

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

DFlash2 shows speed gains but increased memory use for Qwen3.8 27B

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Hefty_Wolverine_553 ·

    I tested DFlash2 for Qwen3.8 27B on a 5090

    <!-- SC_OFF --><div class="md"><p>Here's the <a href="https://inco.ai/blog/dflash2/">DFlash2 announcement</a>, and I was pretty excited for this after trying out DSpark on llama.cpp a few days ago and being somewhat disappointed that it wasn't really working. Anyways, I spent a w…