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Qwen3.8-27B model achieves 2,000 prefill tokens/sec on RTX 3090

By PulseAugur Editorial · [1 sources] ·

A user has achieved significant performance gains for the Qwen3.8-27B large language model on an RTX 3090 graphics card. By implementing a custom kernel that maintains near-fp32 quality at int8 precision, they boosted prefill speed to nearly 2,000 tokens per second and decode speed to 132 tokens per second. These optimizations are available via a GitHub repository. AI

IMPACT Demonstrates potential for significant inference speed improvements on consumer hardware through custom kernel development.

RANK_REASON User-driven optimization of an open-source model, detailed with performance metrics and code. [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 →

Qwen3.8-27B model achieves 2,000 prefill tokens/sec on RTX 3090

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User-driven optimization of an open-source model, detailed with performance metrics and code. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    I pushed Qwen3.8-27B to 2.000 prefill per second and 132 decode per second on A RTX 3090.

    <!-- SC_OFF --><div class="md"><p>Yoyo</p> <p>I'm back with updates to the fastest inference engine with minimal quality loss for Qwen3.8-27B.</p> <p>The last few weeks I've been optimizing decode speed and I don't think it can be pushed further, until a newer/better drafter is i…

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