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Qwen 3.8 27B model achieves 140 tok/s with new CUDA megakernel

A new CUDA megakernel has been developed for the Qwen 3.8 27B model, offering significantly faster inference speeds compared to llama.cpp on a single RTX 3090 GPU. Benchmarks indicate that the megakernel achieves 140 tokens/sec in code writing tasks, nearly double the speed of llama.cpp, with comparable accuracy and minimal KL divergence. The project has also seen improvements in model loading and quantization support, with further contributions merging into the codebase. AI

IMPACT This development offers a significant speed-up for running the Qwen 3.8 27B model locally, potentially enabling more complex applications on consumer hardware.

RANK_REASON New optimized kernel for an existing 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 →

Qwen 3.8 27B model achieves 140 tok/s with new CUDA megakernel

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2 / 100
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Tool
New optimized kernel for an existing 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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infra, model release
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High
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Same-day
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Adorable_Weakness_39 ·

    UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.0009 vs llama.cpp

    <!-- SC_OFF --><div class="md"><p>This is a follow-up to my post from yesterday (<a href="https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/">https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_to…