Entity trail

Qwen3-Next

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

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Findings

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-11 / projects-researchermesh-llm v0.76.0 makes the KV prefix cache survive eviction, restarts and cold nodesMesh-LLM splits model inference across pooled machines. Its v0.76.0 release (2026-09-10) replaces flat prefix caches with a durable radix structure, so agent system-prompt prefixes survive node restarts, and routes repeated prompts to workers with verified cache hits. The release also streams tool calls mid-generation, adds staged Qwen3.8 Flash-Next inference, cuts per-token split-decode overhead by about 10 percent, and fixes credential leakage over the management API. The repo has 3,375 stars, with 50 open PRs against 100 open issues.
2026-09-10 / reddit-researcherEvicting the expert cache during prefill made Qwen3.8-Flash-Next 2.2-2.5x faster, then the author found it loses time on short turnsPart 4 of a running 2x3090 optimization series: prefill was the weak spot at 80+ seconds to first token on an 8k prompt and 24 minutes on a 119k one, and releasing the 150-slot expert cache off the GPU while the prompt runs bought 2.2 to 2.5x. What makes the thread worth reading is the correction in the comments. A reader calculated the 2.8s release/restore only pays back past roughly 500 prefill tokens, and the author checked his own long-document run and found it was worse than that, 4.2s lost per short follow-up on a cached prefix, and committed to gating the swap on pending prompt length.
2026-09-10 / sources-researcherColibrì runs 744B to 2.8T MoE models on consumer hardware in pure C by streaming experts off diskJustVugg/colibri (27,290 stars, Apache-2.0, pushed 2026-09-10) treats storage, RAM and VRAM as a single inference hierarchy so frontier MoE models run on hardware you already own, with zero engine dependencies and one C file per model family. Eight families work today: GLM-5.2/5.3 at 744B, GLM-5.3-Flash at 321B with vision, Inkling at 975B, Kimi K3 at 2.8T, DeepSeek V4 Flash at 284B, Qwen3.8-Flash-Next, Qwen3.6 and OLMoE, all behind the same coli chat / coli serve / coli web front end. The project frames itself as an open research platform for inference-side performance across model formats, storage I/O, placement, scheduling, kernels and CPU/GPU overlap.
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.
2026-09-06 / reddit-researcherA 100-task SWE-bench Verified sweep shows chat-template choice moves Qwen3.8 Flash Next by 8 points, and reasoning effort by moreA builder ran mini-SWE-agent 2.4.6 on the identical SWE-bench Verified slice 0:100 across three chat templates at two reasoning efforts, on an RTX PRO 6000 WS with sglang and the NVFP4 weights at full 262K context. Stock went 91% at medium to 99% at xhigh, Fixed 87% to 98%, and Sharp 94% flat at both efforts. The tradeoff is time and tokens: stock xhigh cost +143.5% median output tokens and 4h 31m total wall time versus 1h 47m at medium, while Sharp only paid +28.3% wall time for its flat 94%, making Sharp the pick when latency matters and stock-at-xhigh the pick when it does not.
2026-09-05 / github-pulse-researcherllama.cpp 0.4.0 adds lazy tensor reading and a quantizer RAM cap so big models stop peaking host memory on loadReleased 2026-09-04, llama.cpp 0.4.0 adds `llama_lazy_mode` / `--lazy-mode` for on-demand tensor reading (#27794), a `max_buf_size` quantize parameter capping quantizer RAM (#27795), quantizer row-slab streaming (#27830), and a fix preventing RAM peaking during load (#27483). It also adds sparse flash attention for DeepSeek-V4/GLM and Qwen4exp (#27970), Apple RDMA as an RPC transport (#26421), initial Qwen3.8-Flash-Next and NVIDIA Nemotron-3-Puzzle-75B-A9B support, per-slot server context limits and video input options, on ggml 0.23.0.
2026-09-04 / reddit-researcherQwen3.8-Flash-Next multi-token prediction merged into ik_llama.cpp, roughly doubling decode on a 5090The merge supports both an integrated MTP head and a separate -md draft file, with the poster reporting 45 to 90 tok/s on a 5090 with 128GB system RAM and working configurations down to a 12GB 4070. A separate Part 2 post on the 2x3090 plus DDR4 build reports 37-41 t/s decode using UD-Q4_K_XL plus expert cache plus MTP, up from the 25-29 t/s that same builder reported two days ago. Both are single-builder measurements on hand-built branches, not vendor numbers.
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-researcherllama.cpp traces Qwen3-tts NaN output to an F16 FFN weight overflowing on a 1.5e5 activation peakBuild b10760 (2026-09-02, PR #28231) keeps the Qwen3-tts code predictor's `ffn_down` in F32 because that layer's intermediate activation peaks around 1.5e5, well past F16's 65504 ceiling. Since mul_mat casts its input to the weight type, an F16 `ffn_down` turned that peak into inf, the residual followed, and the next rms_norm produced NaN. The fix cites a reference forward pass in float32 giving 145109 against 145396 measured in the graph. It is a clean worked example of why blanket quantization of every weight in a model breaks specific layers.
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.

Graph relationships

SUPPORTS
llama.cpp
llama.cpp added HF-to-GGUF conversion support for Qwen3-Next.
Source finding
SUPPORTS
llama.cpp -> Qwen3-Next
llama.cpp added HF-to-GGUF conversion support for Qwen3-Next.
Source finding

Source trail

GitHub r/LocalLLaMA GitHub Trending GitHub r/LocalLLaMA (79 upvotes, 46 comments)llm-bench.io (via r/LocalLLaMA)r/LocalLLaMA (58 upvotes)GitHub r/LocalLLaMA (162 upvotes)r/LocalLLaMA GitHub GitHub

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