--- title: Qwen teases Qwen4 architecture via a surprise Flash-Next... description: Alibaba's Qwen team is releasing Qwen3.8-Flash-Next, described as a preview built on the upcoming Qwen4 architecture rather than a standalone model. Early... canonical: https://daily.dev/posts/qwen-teases-qwen4-architecture-via-a-surprise-flash-next-drop-betdmnvg2 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Qwen teases Qwen4 architecture via a surprise Flash-Next drop | daily.dev og:description: Alibaba's Qwen team is releasing Qwen3.8-Flash-Next, described as a preview built on the upcoming Qwen4 architecture rather than a standalone model. Early... og:url: https://daily.dev/posts/qwen-teases-qwen4-architecture-via-a-surprise-flash-next-drop-betdmnvg2 og:image: https://api.daily.dev/og/posts/bEtdmnVG2.png og:image:alt: Qwen teases Qwen4 architecture via a surprise Flash-Next drop og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Qwen teases Qwen4 architecture via a surprise Flash-Next drop **[Trends](https://daily.dev/sources/trends)** · 2 min read · 2 upvotes · 0 comments ## Summary Alibaba's Qwen team is releasing Qwen3.8-Flash-Next, described as a preview built on the upcoming Qwen4 architecture rather than a standalone model. Early reports suggest roughly 125 billion total parameters in a mixture-of-experts configuration, though activation parameter counts remain unconfirmed. The open-source AI community is treating the drop as a signal of what Qwen4 will bring, with excitement centered more on anticipation than the release itself. ## Content Alibaba dropped Qwen3.8-Flash-Next this week and the reaction has been somewhere between genuine excitement and the usual benchmark skepticism. The short version: 125B parameter MoE model, only 6B active per token, scores 62.5 on SWE-bench Pro against Claude Opus 4.6's 53.4, and you can run it locally on a MacBook with 128GB unified memory. The community is calling it a banger. Some caveats apply. The architecture is the real story here. Alibaba is billing this as an early preview of Qwen4, and the technical choices are genuinely interesting: hybrid Gated DeltaNet plus sparse attention, a 51B n-gram embedding table that lives in regular RAM or SSD rather than GPU memory, and multi-token prediction baked in. That 51B engram layer is what makes local deployment viable on hardware that would normally choke on a 125B model. Antirez (Redis creator) flagged it immediately: "Given that 51B of n-grams can stay on the SSD disk, Qwen 3.8 Flash Next with a 2 bit quantization could be the best DwarfStar bet for a 64GB MacBook local inference top experience." On M5 Max hardware, MLX with speculative decoding hits ~60 tokens/sec using under 90GB memory. llama.cpp gets you half that at 120GB. Someone already got a 180B-class variant running on 39GB via MLX 4-bit quants. NVIDIA's GB300 NVL72 pushes over 16K tokens/sec per GPU. The deployment story is genuinely good. The pushback is quieter but real. One detailed writeup notes that Alibaba's benchmark scores come from their own harnesses, with at least one metric graded by GPT-4o rather than the standard grader. Independent testing on Kingbench put it at 70% versus GLM 5.3 Flash's 78.75%, with the losses concentrated in frontend and 3D tasks. Local models still trail the remote API for serious coding work, per several developers who tested both. There's also a licensing question nobody's fully resolved. The model ships under a qwen-community license while Alibaba plans to charge large commercial users, which may conflict with the EU AI Act's open-source documentation exemption. The "open-weight" framing is doing some heavy lifting here. Still, at $0.16/$0.47 per million tokens via API and genuinely runnable on consumer hardware, the reaction from the open-source crowd is mostly: this is what the ecosystem is supposed to look like. ## Questions this post answers ### What is Qwen3.8-Flash-Next and how does it relate to Qwen4? Qwen3.8-Flash-Next is a preview release from Alibaba's Qwen team built on the new Qwen4 architecture, rather than a standalone model in its own right. Early reports describe it as a mixture-of-experts model with around 125 billion total parameters, though exact activation parameter counts have not been confirmed. It is being treated by the community as an early signal of what Qwen4 will bring. _Developers evaluating open-source LLM roadmaps can follow Qwen4 architecture details as they emerge on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 1 discussion and 39 comments across x (as of 2026-08-30). **TL;DR:** Discussion is dominated by hands-on tinkerers testing and debugging the n-gram/SSD offloading trick from Flash-Next on various consumer Mac and PC rigs, with genuine excitement about the technique alongside skepticism about its real-world throughput and scaling limits. **Sentiment:** 30% positive · 55% mixed · 15% skeptical **The case for** - Some report the SSD-offload/lazy-mmap strategy genuinely works, even on consumer GPU rigs, yielding usable prefill/decode speeds. - Overlapping disk fetch for the n-gram table with first-layer compute is seen as a clever theoretical optimization. - People appreciate that the underlying concept was explained clearly enough for non-experts to follow. **The pushback** - One tester found offloading the n-gram table and cache to external SSD dropped tokens/sec by ~20% versus keeping everything in internal memory. - Several commenters doubt SSD prefetch will hold up under real benchmarks, noting random access patterns on a 51B-entry table could hurt more than raw NVMe bandwidth suggests. - KV cache size, not the quantized weights, is flagged as the likely bottleneck at longer context lengths. **By community** - x (mixed): Enthusiastic experimentation with the SSD/n-gram offloading trick is tempered by pointed skepticism about whether the speed gains hold up under real benchmarks and longer context. **Hottest debate:** Whether offloading the n-gram table to SSD is actually a net win for throughput, given one tester's measured slowdown versus others' optimism about overlapping fetch with compute. **Open questions** - What real generation-speed numbers result from combining split-weight and SSD-offload approaches across multiple smaller machines? - How does prefill and KV cache behavior scale at 2-bit quantization once the context window fills up significantly? - Can the n-gram table access pattern on SSD be restructured (e.g. tiered L1/L2/L3 caching) to avoid random-access penalties? **Highlights** > @antirez @ivanfioravanti I can confirm this strategy actually works, even on my consumer setup: 2× RTX 5060 Ti 16GB, 64GB RAM (DDR5) and NVMe. I’m running UD-IQ4_XS at 98K Q8 KV while keeping the 51B PLE table lazy-mmapped from SSD. On a real 18.2K pi agent prompt i measured 88.8 t/s prefill and 15.8 t/s > — [CostanzoPad on x · 3 points](https://x.com/CostanzoPad/status/2094092409021792486) > @antirez @ivanfioravanti fyi i offloaded ngram and cache to a PCIe4 external ssd and tested flash-next yesterday omlx version, tps speed -20% at 37 tps where ref at 50 tps, same m3 ultra 512gb. then i thought hey why do i waste my time on saving mem. so all back to internal ssd & mem > — [hxiao on x · 1 comments](https://x.com/hxiao/status/2094110349335376263) > @antirez @ivanfioravanti 51B entries on SSD is going to hurt on random access I think. NVMe bandwidth is fine but the access pattern is what kills you. > — [neovectormind on x](https://x.com/neovectormind/status/2094172021194109197) > @antirez @kirahsapong @ivanfioravanti I'd want to see real benchmarks first. SSD prefetch underperforms in practice more often than people admit and that layer 1 compute window on M-series is pretty narrow. > — [neovectormind on x](https://x.com/neovectormind/status/2094173752992878882) > @kirahsapong @ivanfioravanti In theory with a fast SSD like the one you have on a high end MacBook PRO, you should be able to cover the n-gram vector loading *while* you compute the first layer of the model: you already have all the information to do the fetch using the CPU and the disk. Then apply at L2. > — [antirez on x · 4 points, 1 comments](https://x.com/antirez/status/2094165226014773687) **Source threads** - [x](https://x.com/antirez/status/2094065201838510200) · 0 points · 39 comments --- Tags: [#open-source](https://daily.dev/tags/open-source), [#llm](https://daily.dev/tags/llm), [#alibaba](https://daily.dev/tags/alibaba), [#qwen](https://daily.dev/tags/qwen), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts) [View this post on daily.dev](https://daily.dev/posts/qwen-teases-qwen4-architecture-via-a-surprise-flash-next-drop-betdmnvg2) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Qwen teases Qwen4 architecture via a surprise Flash-Next drop","url":"https://daily.dev/posts/qwen-teases-qwen4-architecture-via-a-surprise-flash-next-drop-betdmnvg2","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/qwen-teases-qwen4-architecture-via-a-surprise-flash-next-drop-betdmnvg2"},"datePublished":"2026-08-25T12:51:27.764Z","dateModified":"2026-08-30T22:26:47.825Z","description":"Alibaba's Qwen team is releasing Qwen3.8-Flash-Next, described as a preview built on the upcoming Qwen4 architecture rather than a standalone model. 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Early reports describe it as a mixture-of-experts model with around 125 billion total parameters, though exact activation parameter counts have not been confirmed. It is being treated by the community as an early signal of what Qwen4 will bring. Developers evaluating open-source LLM roadmaps can follow Qwen4 architecture details as they emerge on daily.dev."}}]} ```