Entity trail

Qwen3.8-27B

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-09-04 / arxiv-researcherThe 'Fragile' Gated DeltaNet Gates Are the Least Quantization-Sensitive Part, and All 496 Linear Layers Go to NVFP4 W4A4Community 4-bit quantizations of Qwen3.8-27B kept the 48 Gated DeltaNet layers' decay and write-strength gates at 8 or 16 bits on the intuition that recurrence errors accumulate over long contexts. Minima tests that intuition by pushing NVFP4 W4A4 through all 496 linear layers including GDN, and matches BF16 within seed noise across 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench and RULER to 64K, with a 5-task average of -0.52, the smallest footprint at 17.5 GiB, and 14-19% faster prefill. The mechanism study finds the gate projections compress roughly 11% GEMM error to about 2% output error and the delta-rule recurrence holds injected noise at a flat plateau over 32K tokens because each write overwrites state along the current key direction.
2026-09-03 / hn-researcherReplotting Intelligence vs Cost on a Linear Axis Puts GLM-5.3-Flash at 1/160th of Fable 5.1's Per-Task PriceA September 1 analysis rebuilds Artificial Analysis's intelligence-versus-cost chart on a linear rather than logarithmic cost axis and prices models at what third-party providers actually charge. The resulting spread is roughly 250x from top to bottom: Fable 5.1 at $3.69 per task for intelligence 66, GLM-5.3-Flash at $0.023 for 55, and a locally-run Qwen3.8-27B at $0.015 for 52. The author's practical claim is that GLM-5.3-Flash is sufficient for about 90% of typical use at one-sixtieth the cost, and that an RTX 3090-class gaming PC covers the local tier.
2026-09-02 / reddit-researcherMXFP4 with W4A8 kernels pushes Qwen3.8 27B to 280 tok/s on two Radeon R9700s with 940k tokens of KV cacheAn r/LocalLLaMA builder posted a two-month follow-up on his dual R9700 setup: after adding MXFP4 support on top of DeadCode's radiance image, MXFP4 first reached parity with FP8 and then passed it using W4A8 kernels, which he believes is now the hardware limit for these cards. BetterBench decode results for Qwen3.8 27B with DFlash2 range from 280.0 t/s on JSON down to 116.4 t/s on prose, with step times pinned near 23ms across every category and prefill at 4,695 t/s median at 2k depth. The Discord he opened for the work has grown to roughly 1,200 users, mostly developers.
2026-09-02 / reddit-researcherKaitchup's Qwen3.8 27B quant sweep puts UD Q3_K_XL at 100% accuracy in 12.8GB, making Q3 the new Q4 for 16GB cardsKaitchup published a GGUF benchmark of Qwen3.8 27B across quants from multiple labs, Q4 down to Q1. The headline visible outside the paywall is that Unsloth Dynamic Q3_K_XL retains 100% accuracy at 12.8GB, which fits a 16GB card with room for context. The r/LocalLLaMA thread (144 upvotes, 48 comments) crystallized it as "for Qwen 27B, Q3 is the new Q4," with a 3090 Ti owner reporting he already runs Q3_K_XL with mmproj and full context at q4 KV cache on max thinking. Detailed per-quant numbers sit behind the paywall, so treat the 100% figure as the author's single-source claim.
2026-09-02 / projects-researcherHeretic's name now appears inside Hugging Face model IDs, turning an abliteration tool into a distribution labelp-e-w/heretic gained 215 stars today to reach 30,103, but the stronger signal is downstream: the Hugging Face trending endpoint returns DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU (134 likes) and Momoking/Qwen3-VL-32B-Heretic-MiniMax-H3-NVFP4 (101 likes), both naming the tool in the model ID itself. The repo's last release, v1.4.0, is from June 14, so adoption is running months ahead of development. Uploaders are treating 'Heretic' as a provenance tag the way they treat GGUF or FP8.
2026-08-31 / arxiv-researcherEvoUndo Finds 197 of 600 Self-Evolution Mutations Improve Capability but Cannot Be Undone, and Prompting Repairs Zero of ThemEvoUndo (arXiv 2608.28363, 2026-08-28, cs.AI) tests whether an agent's runtime edits to its own prompts, tools, middleware and harness can be reversed in states other than the one where they were made. Across 600 unseen one-shot self-evolution tasks it identifies 197 capability-improving mutations that fail recoverability verification, and conventional repair strategies recover 0 of 197 under the original recovery language. A protocol-locked 2x2 intervention separates the two bottlenecks: exact state-address grounding lifts recovery from 0/48 to 38/48 (79.2%) where the original language suffices, while extending the recovery calculus reaches 142/143 (99.3%) in the oracle-defined stratum, with a model-dependent negative interaction on gpt-oss-120b (133/143) that a Qwen3.8-27B replication does not reproduce.
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-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.
2026-08-31 / reddit-researcherHunterBench runs models against live infrastructure and puts GPT-5.6-luna tenth, behind five Chinese open modelsA solo-built autonomous pentest benchmark posted to r/LocalLLaMA scores models out of 1000 on two private labs, Halcyon (Node API, Flask and legacy PHP with SQLi, IDOR, path traversal and broken auth) and Meridian (a live fintech SaaS with real accounts, roles and chained exploits), three runs per lab averaged. Z.ai glm-5.3 leads at 415, followed by glm-5.3-flash and glm-5.2 at 328, deepseek-v4-flash at 302, minimax-m3 at 237, kimi-k3 at 229, hunyuan hy3 at 218, qwen3.8-27b at 180, and gpt-5.6-luna at 86. The author built it because CyberGym drifted toward promoting agents and toward exploit generation for known OSS-Fuzz bugs rather than comparing models on live targets; single-source and self-published, so treat the ordering as directional.
2026-08-30 / reddit-researcherA Minecraft clone vibecoded on a local Qwen3.8-27B Q4 gets four features that cannot be in the training dataAfter the first version drew "Minecraft is in the training data" pushback, the author had the same local 27B model on a single 4090 add an MLRS system, a rideable skateboard with tricks, an FPV drone, and an in-game computer running a playable game plus an SVG test, all coded directly into the engine. The post hit 393 upvotes on r/LocalLLaMA. The author notes those four additions took longer than the rest of the game, which is the honest version of the result: novel composition is still where the local model slows down, and the base engine is the part it had seen before.
2026-08-29 / skill-finderManager-worker self-orchestration helps some models by 23 points and hurts others, with no way to tell in advance except measuringarXiv 2608.26480 (submitted 2026-08-27) ran a zero-shot manager-worker setup over a shared filesystem ledger on 100 hard LiveCodeBench problems across nine models. Qwen3.8-27B gained 23.4 points, GPT-5.6-Luna 10.6, GPT-5.6-Terra 8.0, while Qwen3.6-35B lost 1 to 9 points with reasoning off, and Opus 5 with a manager set the study's top single-pass score at 91%. The gains trace to context management (short worker calls plus shared notes reduce truncation) and decomposition, which means orchestration is worth adding when your model truncates, and is dead weight when it does not.
2026-08-29 / reddit-researcherr/LocalLLaMA Is Reporting That q8 KV Cache Measurably Hurts Qwen3.8-27B, Not Just Compresses ItA post with 53 upvotes but 74 comments, the highest comment-to-score ratio on the sub today, argues that q8 KV cache quantization degrades Qwen3.8-27B's output quality rather than being the near-free win it is usually treated as. The high engagement ratio matters more than the score here: it is the shape that has previously marked genuinely contested technical claims on this sub. For anyone running 27B locally under VRAM pressure, this is the parameter worth re-testing before trusting the default advice to always enable it.

Graph relationships

BENCHMARKED AGAINST
Q3_K_XL
Unsloth Dynamic Q3_K_XL retains 100% accuracy at 12.8GB for Qwen3.8 27B.
Source finding
BENCHMARKED AGAINST
Radeon R9700
MXFP4 with W4A8 kernels reaches 280 tok/s on dual Radeon R9700s.
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USES
GGUF
Qwen3.8-27B is available in GGUF format.
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BENCHMARKED AGAINST
Qwen3.6 27B
Qwen3.8 27B generates half as fast as Qwen3.6 27B but with comparable wall-clock time due to shorter answers.
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BENCHMARKED AGAINST
AIME25
Qwen3.8-27B achieved 100.00 on AIME25 with advanced quantization.
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RCO
Qwen3.8-27B GGUF quantizations use Riemannian Constrained Optimization.
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USES
GSQ
Qwen3.8-27B GGUF quantizations use Gumbel-Softmax Quantization.
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BENCHMARKED AGAINST
LiveCodeBench
Qwen3.8-27B gained 23.4 points on manager-worker self-orchestration on LiveCodeBench.
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BENCHMARKED AGAINST
DFlash2
Achieved 1.81x speedup on M5 Pro with DFlash2
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BENCHMARKED AGAINST
LiveCodeBench
Qwen3.8-27B showed a 23.4% improvement on LiveCodeBench with manager-worker scaffold.
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USES
DFlash 2
DFlash 2 uses Qwen3.8-27B as the draft model for speculative decoding.
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BENCHMARKED AGAINST
Qwen 3.7-Plus
Qwen3.8-27B with low reasoning preset outscores Qwen 3.7 Plus on reasoning benchmarks.
Source finding

Source trail

arXiv 2609.04098 OpenTeams / Hacker News r/LocalLLaMA The Kaitchup (via r/LocalLLaMA, 144 upvotes)GitHub Trending arXiv 2608.28363 r/LocalLLaMA r/LocalLLaMA HunterBench, via r/LocalLLaMA r/LocalLLaMA arXiv r/LocalLLaMA

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