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Qwen 3.8 Flash Next runs at high speeds on consumer GPUs · 4 sources tracked

By PulseAugur Editorial · [4 sources] ·

Users are sharing guides and benchmarks for running the Qwen 3.8 Flash Next model on consumer hardware, achieving impressive speeds and quality. One user details a setup on an RTX 5080 with 64GB RAM, reaching over 250 tokens/sec for agent coding sessions. Another showcases performance on an RTX 5090 with 128GB RAM, highlighting quality comparisons and speed metrics. A third user reports success on a 5090 with 64GB RAM, noting a significant prefill boost and the model's capabilities. Finally, a guide demonstrates running the model on a more modest RTX 3060 with 16GB RAM, achieving usable speeds without sacrificing model quality. AI

IMPACT Enables wider accessibility and practical use of advanced LLMs on consumer-grade hardware.

RANK_REASON User-generated guides and benchmarks for running specific LLM models on consumer hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Qwen 3.8 Flash Next runs at high speeds on consumer GPUs · 4 sources tracked

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0 / 100
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User-generated guides and benchmarks for running specific LLM models on consumer hardware.
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4 independent sources
Strong cross-source corroboration — multiple independent publishers covered this within the clustering window.
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model release, product
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10 days old
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Full methodology in our editorial standards.

COVERAGE [4]

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

    [GUIDE] Qwen3.8 Flash-Next on a single RTX 5080 + 64GB RAM: >250 tok/s tg and 5700 tok/s prefill in real agent coding sessions (Basalt + new Unsloth UD-IQ3_XXS + Lamina drafter),+ how it compares to llama.cpp

    <!-- SC_OFF --><div class="md"><p><strong>TL;DR</strong></p> <ul> <li><strong>Basalt</strong> is the engine and it's doing the heavy lifting. Same model file, same box: about <strong>3.5x llama.cpp's decode without drafting and ~5x with it, and ~7x faster prompt reads</strong>.</…

  2. r/LocalLLaMA TIER_1 English(EN) · /u/jesdga95 ·

    Basalt 0.2.0: Qwen 3.8 Flash Next NVFP4, 300 t/s prose, 600 t/s struct, 10K t/s prefill on a 5090 + 5060 Ti and 128GB RAM

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xn4qn1/basalt_020_qwen_38_flash_next_nvfp4_300_ts_prose/"> <img alt="Basalt 0.2.0: Qwen 3.8 Flash Next NVFP4, 300 t/s prose, 600 t/s struct, 10K t/s prefill on a 5090 + 5060 Ti and 128GB RAM" src="https://pre…

  3. r/LocalLLaMA TIER_1 English(EN) · /u/Rozzemak ·

    Qwen 3.8 Flash Next NVFP4 on 64gb RAM and 5090, Basalt. Ditched windows for it. Very hyped for qwen 4 model family now.

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xlicia/qwen_38_flash_next_nvfp4_on_64gb_ram_and_5090/"> <img alt="Qwen 3.8 Flash Next NVFP4 on 64gb RAM and 5090, Basalt. Ditched windows for it. Very hyped for qwen 4 model family now." src="https://external…

  4. r/LocalLLaMA TIER_1 English(EN) · /u/zyxciss ·

    Betelgeuse: Running Qwen 3.8 Flash Next at 22 tok/s sustained (spiking to 48 tok/s) on an RTX 3060 12GB + 16GB DDR4 RAM 100% bit-exact!

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xk8s6z/betelgeuse_running_qwen_38_flash_next_at_22_toks/"> <img alt="Betelgeuse: Running Qwen 3.8 Flash Next at 22 tok/s sustained (spiking to 48 tok/s) on an RTX 3060 12GB + 16GB DDR4 RAM 100% bit-exact!" sr…

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