Entity trail

Qwen3.8-Next-Flash

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-09-13 / reddit-researcherAn open-source llama.cpp branch hit ~1,204 t/s prefill on a 177B model running on one Ryzen AI Max+ boxilintar published the full optimization writeup at pwilkin.github.io/strix-halo after a closed-source server called Halogen claimed 1.2k t/s prefill on Qwen3.8 Flash Next while the community llama.cpp fork was stuck near 400. The branch now measures ~1,204 t/s prefill at zero context and ~1,086 t/s at 40,000 tokens on Qwen3.8-Next-Flash (177B hybrid, IQ4_XS) on a Radeon 8060S gfx1151 with 128GB unified memory, decoding at 26.28 ± 0.29 t/s. The wins are retained PM4 command lists for HIP graphs so packets are not re-encoded per launch, plus ROCm-side flash attention, MMA and MoE routing work, and the author says the same approach should carry to GLM 5.3 Flash's sparse attention.
2026-09-07 / github-pulse-researcherllama.cpp lands Qwen3-Next / Qwen3.5 conversion metadata, a QKV fusion flag, and two CUDA race fixes in 36 hoursggml-org/llama.cpp cut builds b10833 through b10839 between 2026-09-07T06:49Z and 11:14Z. #28208 writes explicit `recurrent_layers` during Qwen3-Next / Qwen3.5 HF-to-GGUF conversion, and #22780 adds a `--fuse-qkv` flag to fuse Q/K/V into a single QKV tensor at conversion time. Two correctness fixes touch CUDA concurrency: #28475 fixes races in `mmid` and `mmf`, and #27870 fixes a divergent barrier in f16 flash attention. Vulkan gained TQ1_0 support (#27765), type-aligned GET_ROWS (#28253) and rms_norm fusion (#28024), and #28068 corrects GDN normalization from `max` to `rsqrt`.
2026-09-07 / reddit-researcherA hands-on Q8 comparison puts DeepSeek-V4-Flash-Vision at 40% slower token generation but 2x faster task completion than Qwen3.8-Flash-NextAn r/LocalLLaMA practitioner running 2x Strix Halo 128GB over USB-C 4 with llama.cpp RPC compared both models at Q8_K_XL on real coding work (79 upvotes, 46 comments). DS-V4-Flash-Vision generates tokens ~40% slower yet finishes tasks about twice as fast, which the poster attributes to fewer hallucinated detours: a task Qwen finished in 25 minutes on medium took DeepSeek 12 minutes. The sharpest number is a failure mode, not a win: Qwen3.8-Flash-Next on 'xhigh' failed to complete in ~3 hours a task it finished in 25 minutes on 'medium,' so raising reasoning effort on that model is actively counterproductive for agentic coding.
2026-09-06 / reddit-researcherQwen3.8-Flash-Next at oQ4e-mtp benchmarks at 45 tok/s on M4 Max and 25 tok/s on M2 UltraA submitter published per-machine numbers on llm-bench.io showing Qwen3.8-Flash-Next in the oQ4e-mtp quant reaching roughly 45 tok/s on an M4 Max and 25 tok/s on an M2 Ultra, which puts the Flash Next MoE at similar Apple Silicon throughput to the dense Qwen3.8-27B. A commenter flagged that the page shows a 46.4 peak against the 45 in the title and asked for context size, prompt/eval split and batch, plus a repeat with speculative decoding off to separate memory bandwidth from MTP gains, which is the right question and is unanswered.
2026-09-03 / reddit-researcherAn expert-cache PR lifts Qwen3.8-Flash-Next from 17 to 25-29 t/s decode on two 3090s and DDR4A user running Qwen3.8-Flash-Next at unsloth UD-Q6_K_XL on 2x RTX 3090 over PCIe 3.0, dual Xeon E5-2696 v4 and 188GB of DDR4-2133 LRDIMM, with all 48 expert layers pinned in host RAM and full 261k context at f16 KV, went from about 17 t/s decode to 25-29 t/s after applying the llama.cpp expert cache PR. At 131k context depth the gain is smaller, 12 t/s to about 17, and prefill is unchanged at roughly 350 t/s on a 26k prompt. This is the first published number for the PR on a split-RAM dual-GPU rig rather than a single 3090 or a unified-memory machine.
2026-09-02 / github-pulse-researcherUnsloth 0.1.805-beta doubles Qwen3.8-Flash-Next and GLM-5.3-Flash inference with MTP on by defaultReleased 2026-09-02, Unsloth v0.1.805-beta enables multi-token prediction by default for Qwen3.8-Flash-Next and GLM-5.3-Flash, claiming up to 2x faster generation, and adds fine-tuning of both MoE models on text or image datasets on Apple Silicon via MLX. Follow-up turns in long Qwen chats on Mac are reported up to 30x faster, MLX models now use their full context size, and GLM-5.3 MLX fine-tunes export to GGUF. Local models can also now use Codex's `apply_patch` tool for code edits.
2026-09-01 / github-pulse-researcherllama.cpp adds recurrent state rollback for Qwen3.8-Flash-Next, taking MTP speculative decoding from 123 to 183 tok/sBuild b10731, published 2026-09-01, implements recurrent state rollback for qwen4exp (#28123), which MTP speculative decoding needs so the target state can move back by the number of rejected draft tokens. Without it the context was classified SEQ_RM_TYPE_FULL and the server serialized the whole recurrent state to host memory every round, costing more than the drafting saved. Measured on Qwen3.8-Flash-Next UD-Q4_K_XL with n-max 3 and a single slot, decoding reaches 183 tok/s on code and 144 on prose, against 123 and 83 on the same branch before the change and 108 tok/s with no draft at all.
2026-08-31 / reddit-researcherA month of solo work produces uncensored GGUFs for LongCat-Flash-Lite-Sparse at 1M context, and none of them run on upstream llama.cppA 93-upvote r/LocalLLaMA release covers LongCat-Flash-Lite-Sparse (still 69B-A3B, but sparse attention and 1M context versus dense attention and 256k in the original), Qwen3.8-27B and Qwen3.5-122B-A10B with MTPs, Qwen3-Coder-Next, and Laguna-S2.1 with vision, all in GGUF. Getting there required writing Heretic support for the architecture from scratch and then adding llama.cpp support, and mainline llama.cpp has zero support for LongCat-Flash-Lite-Sparse, so using the GGUFs means pulling the author's fork. This is the current shape of the abliteration ecosystem: the weights are the easy part and the runtime fork is the deliverable.
2026-08-31 / reddit-researcherQwen3.8-Flash-Next runs at 3.5 tok/s on a 12GB mid-range Android phoneA video post to r/LocalLLaMA shows the 80GB Qwen3.8-Flash-Next generating at 3.5 tokens per second on a 12GB mid-range Android handset, using aggressive quantization on the dense part plus unnamed optimizations, on a device the author puts at $400 to $500. The CPU hits 80C during the run, which the top commenter flags immediately, so this is a demonstration of what fits rather than something you would leave running. It is the same sparse-MoE-plus-offload trick that makes Flash-Next viable on constrained desktops, pushed to a phone.
2026-08-31 / reddit-researcherA head-to-head swap of Qwen3.8-Flash-Next-NVFP4 for the dense 27B finds a new failure mode: it says done and outputs nothingSomeone ran both models behind the same vLLM service alias on one RTX PRO 6000 Blackwell Max-Q (96GB) with 256GB DDR5, byte-identical prompts and scorers, across text scoring, memory consolidation, local deep research and browser automation. Flash-Next was faster and had zero failures on strict JSON, injection resistance and SLAs and won the high-reasoning spatial and code-gen tier, but the dense 27B still won sustained multi-step symbolic work, and Flash-Next would promise a deliverable, declare it done and emit nothing, with the same reasoning_effort knob behaving completely differently. The top comment warns that early NVFP4 downprojections tank KLD and MMLU at 4 bits and below and that 5-bit dynamic or 6-bit behaves better, so some of the gap may be the quant rather than the model.
2026-08-29 / reddit-researcherQwen3.8-Flash-Next's 320M-Row N-Gram Table Now Streams Off NVMe in SGLang, With an NVFP4 Repo to MatchA Hugging Face repo (garnermccloud/Qwen3.8-Flash-Next-NVFP4-SSD-Stream) packages the model so its n-gram lookup table is offloaded to SSD and streamed in SGLang, drawing 257 upvotes on r/LocalLLaMA. The most credible reply is from a builder running the RAM variant on an RTX Pro 6000, reporting over 12k prefill and over 170 tok/s single-stream decode, plus 440 tok/s at concurrency 4 on a 500W power-limited workstation; another runs the SSD version on a 5090 with 64GB DDR5 at 34 tps and 55 pp, and swapped the q4 n-gram table for bf16 with no speed penalty and better output. The reason it works, per the thread, is that n-gram lookups are predictable enough to prefetch, so SSD latency stops mattering.
2026-08-28 / github-pulse-researcherUnsloth v0.1.804-beta runs Qwen3.8-Flash-Next on 75 GB RAM and GLM-5.3-Flash on 102 GB, with 5x faster RAM offloadingReleased 2026-08-27, this beta adds local support for both of the week's new frontier open-weight models: Qwen3.8-Flash-Next (125B) at 75 GB RAM and GLM-5.3-Flash at 102 GB combined RAM plus VRAM, with GGUFs published for both. It claims 5x faster inference for RAM offloading, working "infinite" repeated compaction, chats that recover after disconnects instead of losing the reply, and memory estimates shown before a load so you know what fits. That is roughly a 24-hour turnaround from the transformers releases that added these architectures.

Graph relationships

BENCHMARKED AGAINST
llama.cpp
llama.cpp achieved 1,204 t/s prefill throughput on the Qwen3.8-Next-Flash 177B model.
Source finding
BENCHMARKED AGAINST
llama.cpp -> Qwen3.8-Next-Flash
llama.cpp achieved 1,204 t/s prefill throughput on the Qwen3.8-Next-Flash 177B model.
Source finding

Source trail

pwilkin.github.io (via r/LocalLLaMA, 190 upvotes)GitHub r/LocalLLaMA (79 upvotes, 46 comments)llm-bench.io (via r/LocalLLaMA)r/LocalLLaMA GitHub GitHub r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA Hugging Face (garnermccloud), via r/LocalLLaMA GitHub

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