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Qwen 3.8 Flash Next model performance discussed across various local setups · 6 sources tracked

By PulseAugur Editorial · [7 sources] ·

Users on Reddit's r/LocalLLaMA subreddit are discussing their experiences running the Qwen 3.8 Flash Next model with various hardware configurations and software optimizations. Discussions cover achieving high context lengths, such as 130k or even 1 million tokens using techniques like YaRN, and optimizing token generation speeds. Users are experimenting with different quantization methods (e.g., IQ2_XS, IQ3_S, OQ4E) and inference engines (e.g., oMLX, Strata, Infernix) to maximize performance on consumer-grade GPUs and limited RAM. AI

IMPACT Users are sharing optimization techniques and performance metrics for running large language models on consumer hardware, potentially lowering the barrier to entry for advanced AI experimentation.

RANK_REASON Cluster consists of user discussions and benchmarks on Reddit about running a specific open-source model, not an official release or research paper.

Read on Towards AI →

AI-generated summary · Google Gemini · from 7 sources. How we write summaries →

Qwen 3.8 Flash Next model performance discussed across various local setups · 6 sources tracked

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Cluster consists of user discussions and benchmarks on Reddit about running a specific open-source model, not an official release or research paper.
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model release, infra
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COVERAGE [7]

  1. Towards AI TIER_1 English(EN) · Ankit Agrawal ·

    Qwen3.8-Flash-Next: Which Quant to Run From 16GB to 128GB in 2026

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/qwen3-8-flash-next-on-strata-which-quant-to-run-from-16gb-to-128gb-in-2026-843778d218a1?source=rss----98111c9905da---4"><img src="https://cdn-images-1.medium.com/max/2600/1*Dehmkz8IlwyBc…

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

    What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length and a reasonable amount of tok/sec?

    <!-- SC_OFF --><div class="md"><p>As i use a rather tiny setup 12gb vram gpu and 32bg ram, i was wondering: What setup do you use, which harness? and how do you customize it to run qwen 3.8 flash next swift with a high context length and a reasonable amount of tok/sec?</p> </div>…

  3. r/LocalLLaMA TIER_1 English(EN) · /u/chibop1 ·

    OMG! If you have a Mac with 64GB, try Qwen3.8-Flash-Next-oQ4e-mtp with oMLX!

    <!-- SC_OFF --><div class="md"><p>I'm genuinely shocked! I was able to run Qwen3.8-Flash-Next-oQ4e-mtp on M3Max 64GB with oMLX!</p> <p>Yes, there are other 3bit quants for 64GB Mac, but 4bit is the lowest quant I'd tolerate. I manually quantized the original model from the Qwen r…

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

    Is anyone running Qwen3.8 Flash Next with a 1M context?

    <!-- SC_OFF --><div class="md"><p>So, while doing a search to read the model card again, and because I didn't memorize the huggingface URL, I saw an AI &quot;answer&quot; at the top of the search, stating that while it natively supports a ~244k context, it could go to 1M using Ya…

  5. r/LocalLLaMA TIER_1 English(EN) · /u/zipzak ·

    Qwen Flash Next @ 137 tok/s & 3,497 tok/s Prefill w/ 512k context on a 5090, 192gb ram, Windows Build, comparing Strata and Infernix

    <!-- SC_OFF --><div class="md"><p><strong>Strata vs Infernix on the same model, RTX 5090 — A/B at 262k and 512k context</strong></p> <p>Hi folks, another inference engine post for your feed. I was blown away by Strata, but saw someone else post about Infernix with some wild claim…

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

    Qwen 3.8 Flash Next-GSQ-RCO-IQ2_XS at ~21 tok/s on just an RTX 3060 12GB + 16GB DDR4 RAM(No gate pruning, 100% bit-exact)

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1x1tclb/qwen_38_flash_nextgsqrcoiq2_xs_at_21_toks_on_just/"> <img alt="Qwen 3.8 Flash Next-GSQ-RCO-IQ2_XS at ~21 tok/s on just an RTX 3060 12GB + 16GB DDR4 RAM(No gate pruning, 100% bit-exact)" src="https://pr…

  7. r/LocalLLaMA TIER_1 English(EN) · /u/bodhi371 ·

    Qwen3.8-Flash-Next-GSQ-RCO (IQ3_S): ~20-30 tok/sec decode & 300-90k tok/sec prefill on 12GB VRAM + 32GB RAM + NVME

    <!-- SC_OFF --><div class="md"><p>I got Qwen3.8-Flash-Next-GSQ-RCO-Abliterated running at IQ3_S with just 12GB VRAM and 32GB system RAM, achieving 20-30 tok/sec decode (Q2 achieves 39-45 tok/s) &amp; 300 to ~90 thousand tok/sec prefill @ 131k context, on a custom fork of Strata.<…

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