Entity trail

Qwen 3.8 27B

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

Briefing refs

Findings

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Sources

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

Corpus findings

2026-09-10 / arxiv-researcherA Fine-Tuned 4B Qwen in 2.6 GB Beats GPT-5.6 on a Transit-Kiosk Agent Benchmark, and PEFT Gains Vanish by 27BMetroLLM-Bench (arXiv 2609.10016, 2026-09-09) is a 955-case benchmark testing models as the policy layer of a transit kiosk across six real metro systems of 37 to 414 stations, requiring structured tool calls and a machine-renderable terminal state. On the 238-case held-out split, a PEFT-trained 4B Qwen 3.5 student scores 91.3 on Tier 1 against GPT-5.6's 90.6 and 90.0, in a 2.6 GB Q4_K_M footprint, while 9B and 27B students add nothing further. The PEFT gain over base shrinks monotonically from +7.03 points at 2B to -0.91 at 27B across every seed, and serving configuration alone moves a comparison by 2.7 points. Released at github.com/continker/metrollm-bench.
2026-09-09 / reddit-researcherQwen 3.8 27B was pushed to build a 3D FPS game from a plan written by Fable 5An r/LocalLLaMA experiment (217 upvotes, 84 comments) took a plan generated by Fable 5 and handed the implementation to Qwen 3.8 27B running through the PI agent, testing how far a 27B local model gets on 3D graphics work. The setup, frontier model plans, local model executes, is the practical shape of the cost-split people keep theorizing about, and the thread is a concrete data point on where a 27B ceiling actually sits. It was inspired by a Bijan Bowen Subway FPS video, so treat it as a demo rather than a benchmark.
2026-09-09 / hn-researcherA Ten-Way Model/Harness Bake-Off on One Three.js Scene Shows a 47x Cost Spread and a 4x Time Spread for the Same TaskPublished 2026-09-09, the test gave ten model/harness pairs the same prompt: a single-page Three.js sci-fi hangar with hovering drones, animated warning lights, emissive runway strips and volumetric-style fog planes. GLM 5.3 Flash on OpenCode finished cheapest at $0.085234 with 96.89% cached input; Astra 6.0 Max on Codex cost $4.038190 and took 37m29s. Qwen 3.8 27B x-high on OpenCode was fastest at 8m48s. Harness choice, not model choice, drives most of the spread, which matches the ARC Prize and Armature results from last week.
2026-09-08 / saas-disruption-researcherTen Model/Harness Combinations on One Three.js Task Show a 4x Wall-Clock Spread and No Correlation With Model TierAn independent test published 2026-09-07 ran the same prompt (a single-page Three.js sci-fi hangar with hovering drones, warning lights, emissive runway strips and volumetric fog planes) across ten model/harness pairs spanning Codex, OpenCode, OMP and DSH/PTC with GLM 5.3 Flash Max, Luna 5.6 Max, SOL 5.6 Max, Astra 6.0 Max and Qwen 3.8 27B. Qwen 3.8 27B on OpenCode finished in 8m48s while Astra 6.0 Max on Codex took 37m30s, a 4.3x spread; Luna 5.6 Max on Codex used the fewest tokens at 1,172,267 and GLM 5.3 Flash Max on OpenCode hit 96.89% cached input. The harness, not the model tier, dominated both wall-clock and cache efficiency, which is the variable most agent-tooling comparisons never isolate.
2026-09-08 / reddit-researcherA task-aware quantization pipeline puts Qwen3.8-27B at 99% of BF16 reasoning at 15% of the size, beating byte-matched Unsloth by 5.5 pointsTAK (Task Aware Knapsack) builds an imatrix from a task-specific corpus, finds the smallest size before collapse, then promotes and demotes tensors within a byte budget, with no pruning, fine-tuning or merging. On held-out reasoning data it scores 82.81% against 83.59% for BF16 and 77.34% for the byte-matched Unsloth UD IQ2_S, with larger gaps on smaller models: +11.72 on Qwen3.5-4B, +14.06 on Gemma 4 E4B, +19.53 on Gemma 3 4B QAT. The author edited the post to warn that coding is outside the reasoning-specialized quant's domain after users hit repetition loops.
2026-09-07 / reddit-researcherA €4,000 dual-R9700 build runs Qwen 3.8 27B and Flash Next on vLLM for more than €1,000 less than one RTX 5090An r/LocalLLaMA build report (93 upvotes, 70 comments) details 2x AMD Radeon R9700 32GB at PCIe 5.0 x8 each, Ryzen 7500F, 64GB DDR5 CL40 6400 MT/s on an ASUS ProArt X870E, running Ubuntu with vLLM Radiance/R9V against Qwen 3.8 27B in FP8 and MXFP4 plus Flash Next. Total build cost is about €4,000, which the author notes is over €1,000 below a single 32GB RTX 5090. Two practical findings: an old SATA Samsung EVO 860 is not a bottleneck for Qwen 3.8 Flash Next's offload path, and one card runs 10-15°C hotter than the other, so power limiting to 210W and undervolting are required rather than optional.
2026-09-07 / reddit-researcherAbliterlitics published a 167-GPU-hour forensic audit of 8 uncensored Qwen 3.8 27B variants and found one card's claims match its weightsThe report at abliterlitics.dev, posted to r/LocalLLaMA at 530 upvotes, ran 11 days and ~167 hours on an RTX 5090 (91h LM-Eval, 75.5h HarmBench, 0.7h KL divergence, 3.3 CPU hours weight analysis) across weight comparison, 13 benchmarks, KL divergence and HarmBench's 400 classic behaviors with an LLM judge. Rankings by attack success rate: orcarouter 82.2%, apostate 78.7%, huihui 75.6%, ultra_heretic 70.5%, coder3101 70.0%, blackfrost 68.5%, obliteratus 63.9%, trohrbaugh 57.5%, against a 4.5% base. Capability cost tracks separately from ASR: apostate has the lowest KL at 0.0439 while obliteratus sits at 1.5427, and the author says orcarouter was the only card where every published claim checked out against the actual weights.
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 production RTX 5090 bake-off pits NInfer and vLLM NVFP4 against llama.cpp GGUF on a six-tier custom evalA builder serving Qwen3.8-27B for a content-intelligence pipeline moved off llama.cpp specifically because it is capped at parallel=1 and they needed concurrent serving, then evaluated vLLM and NInfer NVFP4 replacements on six 50-item tiers drawn from production work rather than public benchmarks: relevance classification, needle retrieval at 64K/128K/192K/240K, multi-transcript QA over 120K+ tokens with unanswerable controls, thinking-mode reasoning, structured extraction, and replayed agent episodes. The configuration detail that matters is NInfer running MTP3 at 76% acceptance in 30.5 GB and llama.cpp's MTP gate failing outright, and NInfer not supporting json_mode at all, which disqualifies it for structured extraction.
2026-09-05 / reddit-researcherA Qwen3.8-27B user reports 8+ hours of unsupervised agentic work with a 2048-token reasoning budgetThe top-voted claim in r/LocalLLaMA today (359 upvotes, 175 comments) is that Qwen3.8-27B is the first local model the poster trusts to run unattended, with the config spelled out in an edit: huihui abliterated Q3_K_XL, reasoning budget capped at 2048 and still coherent at 1024, KV cache at Q8 with 128k context, and a custom chat template plus reasoning-format deepseek to fix tool-call and think-tag generation. Two other threads corroborate the agentic angle, including a run where the model reached a target Wikipedia article in 6 Playwright clicks with no backtracking. This is a single practitioner's report, not a benchmark, but the failure modes named (tool tag generation, compaction) are the ones that actually stop local agent loops.
2026-09-05 / reddit-researcherSomeone benchmarked 21 Qwen3.8-27B quants on a 16GB card by mean KLD against real C codeAn RTX 5080 owner ran 21 community quantizations of Qwen3.8-27B on their own C codebase, ranking by mean KL divergence and same-top-p agreement rather than a public benchmark. bartowski/Qwen3.8-27B-IQ4_XS won overall, with huihui-ai's abliterated UD-IQ4_XS the best uncensored option and jpetrina's IQ4_XS-pure the choice when you need more context headroom. The bottom of the table is the useful part: a QAT q2_0 at 8.2GiB carries a 0.89 mean KLD and only 85.7% top-p agreement, so the cheapest quants are far worse than their file size suggests.
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.

Graph relationships

USES
Pi
Qwen 3.8 27B ran through the PI agent framework.
Source finding
BENCHMARKED AGAINST
Fable 5
Fable 5 generated a 3D FPS game plan that Qwen 3.8 27B then implemented.
Source finding
SUPPORTS
llama.cpp
llama.cpp supports inference of Qwen 3.8 27B with extreme quantization for long context
Source finding
BENCHMARKED AGAINST
MTPLX
MTPLX achieves roughly 3x faster generation on Qwen 3.8 27B without a separate draft model.
Source finding
USES
OpenCode
Qwen 3.8 27B on OpenCode was fastest at 8m48s.
Source finding
BENCHMARKED AGAINST
LM-Eval
Qwen 3.8 27B uncensored variants were evaluated with LM-Eval benchmarks.
Source finding
BENCHMARKED AGAINST
HarmBench
Qwen 3.8 27B uncensored variants achieved attack success rates from 57.5% to 82.2% on HarmBench.
Source finding
USES
vLLM
Qwen 3.8 27B runs on vLLM in the €4,000 dual-GPU build.
Source finding
USES
CORS Chat
CORS Chat was tested running Qwen 3.8 27B via LM Studio on an M5 MacBook.
Source finding
BENCHMARKED AGAINST
DFlash 2
DFlash 2 quantizer supports Qwen 3.8 27B with 30% claimed faster inference.
Source finding
BENCHMARKED AGAINST
Fable 5
Qwen 3.8 27B outperforms Fable 5 on German translation.
Source finding
BENCHMARKED AGAINST
GPT-5.6
Qwen 3.8 27B outperforms GPT-5.6 on German translation.
Source finding

Source trail

arXiv 2609.10016 r/LocalLLaMA alvins82 (via Hacker News, 97 points)r/LocalLLaMA (167 upvotes, 49 comments)r/LocalLLaMA (93 upvotes, 70 comments)Abliterlitics (via r/LocalLLaMA, 530 upvotes, 159 comments)llm-bench.io (via r/LocalLLaMA)r/LocalLLaMA (64 upvotes)r/LocalLLaMA (359 upvotes)r/LocalLLaMA (223 upvotes)arXiv 2609.04098 Cerebras Inference Docs

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