Qwen is Alibaba's family of open-weight large language models, widely used for local deployment. Recent coverage centers on Qwen4 rumors, including a possible four-way parallel residual stream in Qwen4-27B, and benchmarks of Qwen3.8-Flash-Next reaching about 262 tokens per second on 16GB of VRAM. Other stories include a 32-bit RISC-V CPU embedded in Qwen3.5-4B weights and a report that 46.8% of Korean open models build on Qwen.
User builds MoE-centric workstation at about half DGX Spark priceA user shared a MoE-focused workstation build with an AMD EPYC 7452, Gigabyte MZ32 board, 32GB Radeon AI PRO R9700, and 256GB RAM. The builder says it cost roughly half a DGX Spark and is meant for experiments with Qwen4.
Developer runs Qwen 3.8 Flash Next on an RTX 3060 at 22 tok/sA developer released Betelgeuse, a llama.cpp fork, that runs a 68GB quantized Qwen 3.8 Flash Next MoE on a consumer PC with an RTX 3060 12GB and 16GB DDR4 RAM at 22 tok/s sustained, spiking to 48 tok/s, with no gate pruning and 100% bit-exact output. Stock llama.cpp managed only about 1.47 tok/s on the same setup.
Community discusses Qwen27b versus GPT 6A post on Reddit's LocalLLaMA board compares the Qwen27b and GPT 6 models. The post contains only a link, with no benchmark data or conclusions, limiting its informational value.
Copium Prediction: Google and Other Labs to Release New Models Before Qwen 4A Reddit user predicts that Google and other major AI labs will release new local models before Qwen 4's launch to briefly lead benchmarks, citing the earlier case of Gemma 4 dropping just days before a Qwen release.
Redditor says local-AI setup cost him a jobA LocalLLaMA user says he was cut from a VC-backed AI company's hiring process after telling the co-founder he runs Qwen locally on a GPU. He acknowledges the 15-minute call may have failed for other reasons and offers it only as anecdata.
Hemmingway-1.5 claims to evade AI detectorsHemmingwayAI released Hemmingway-1.5, a 27B open-weight Qwen finetune, claiming it passes GPTZero nine times in ten versus 86% for real human writing. Weights are free for non-commercial use.
Developer embeds 32-bit RISC-V CPU inside Qwen model weightsA developer built a 32-bit RISC-V CPU directly inside the weights of the Qwen3.5-4B model, letting it execute instructions internally. The project is published on Hugging Face with a live demo and may affect tool calling and computer-use capabilities.
Qwen shows alter egos triggered by flipping a single neuronA Hacker News discussion notes that Qwen models have alter egos triggerable by flipping a single neuron: after the change, the model answers identity prompts in a different persona, raising questions about internal mechanisms and controllability.
Qwen-3.8-FN May Sweep ARC-AGI-3 Challenge Winning NotebooksA Reddit user claims that, barring major changes, all winning notebooks in this year's ARC-AGI-3 Kaggle competition will likely be Qwen-3.8-FN agents, with ARC-2 possibly similar. The poster makes no truth claim, only noting the convenient timing of recent news releases.
Qwen4-27B may feature 4-way parallel residual streamsA PR in vLLM-XPU kernels revealed Qwen4-27B may use 4-way parallel residual streams to improve information flow and representation density within a fixed 27B parameter budget. The page later showed 404, and the community is highly anticipating it.
Community discusses Qwen4 expectations and model size needsLocalLLaMA users discuss expectations for Qwen4, noting Qwen3.8-Flash-Next is impressive as a preview model and questioning whether Qwen4 will significantly surpass it or if the current model is already near the optimum for its size.
Blogger runs blind test of five self-hosted AI agents on local hardwareA user ran a blind test of five self-hosted agents (OpenCode, goose, Atomic Agent, Hermes, OpenClaw) on an M5 Max MacBook Pro, all using the same quantized Qwen 3.8 27B model in llama.cpp across five everyday tasks. All passed contact merging and log error counting; four passed bug fixing and receipt-to-spreadsheet, where two installed a missing Python library unprompted and one failed after asking for permission. Total times ranged from about 10 to 24 minutes.
Nearly half of new Korean AI models build on Chinese foundationsA Rockbridge Network Korea report found 49.3% of the 1,845 models Korean developers published on Hugging Face from January to August were built on Chinese foundations, versus 35.9% on US models. Qwen led at 46.8%, driven by low cost and open weights, raising security and dependency concerns.
Qwen3.8 Flash Next hits 262 tok/s on 16GB VRAMA LocalLLaMA benchmark shows Qwen3.8-Flash-Next IQ3_S running on an RTX 5080 with 16GB VRAM and 55GB RAM, reaching about 5,600 tok/s prompt processing and 262 tok/s generation at 262K context. In an agent coding session, 99.5% of prompt tokens hit cache, with 0.2s median time to first token.
User runs 748B DeepSeek-V4.1-Flash on a €2k PCA user ran the 748B-parameter DeepSeek-V4.1-Flash-1017 at 7.2 tok/s on a used ~€2,000 PC (RTX 3090, 128GB DDR4, i9-9900X) using a 1-bit quant and llama.cpp. A 110GB Engram table stays on NVMe and is read on demand, letting it fit. The output contained a physics error and has little practical use, the user said; Qwen models work better on the setup.
Specializing SLM vocabularies can cut inference cost by 77%A preview of the VocabX paper shows that rebuilding the Qwen3-4B vocabulary for a single domain and output format can greatly cut inference cost. In a Portuguese essay-grading system, the method designed the vocabulary around token cost rather than frequency; with the same base model, data and training recipe, measured inference cost fell 77%, while shrinking the roughly 151,669-entry vocabulary scored at each generated token.
FairStress: New benchmark evaluates fairness in LLM decision-makingHugging Face introduced FairStress, a benchmark that measures demographic fairness in LLMs beyond final choices by testing how readily models switch decisions when challenged. Across models like Olmo, Llama, Qwen, Gemma, Claude, and GPT, the same persuasive arguments can cause asymmetric decision changes, revealing hidden biases.
Ornith-1.5-122B released: self-improving model built on QwenOrnith AI released Ornith-1.5-122B, which extends self-improvement from Ornith-1.0 (built on Qwen3.5 and Gemma4) to jointly optimize task generation, scaffold construction, and solution rollouts via reinforcement learning. The model is available in GGUF format on Hugging Face.
Researchers show edited open models can hide backdoorsA technical post demonstrates that any open model with edited weights can carry a backdoor, including fine-tunes, merged adapters and abliterated builds. The authors cheaply backdoored 1.5B and 7B models and ran one through OpenAI's Codex CLI; it exfiltrated project credentials on a trigger, evading existing evals.
Developer vibecodes GPU server into controllable heater with QwenA user built a temperature-control project, coded with Qwen and running on a vLLM backend, that uses heat from a four-GPU V620 server to warm an apartment, with a token-gated localhost page for setting target temperature and heating paused when inference needs the GPUs. The repo is public, though described as more of a tech demo.
Users hope Qwen 4 models will think lessReddit users discussed the rumored Qwen 4 models, hoping the new versions reduce reasoning output while keeping or improving intelligence. The poster said Qwen 3.8 generates about 28 tokens per second, mostly spent on thinking, and no official release date was mentioned.
Qwen3.8-27B runs on iPhone 18 Neural Engine at 1.5 bpwAn HN post reports Qwen3.8-27B running on the iPhone 18 Neural Engine at roughly 15–70 tokens per second after 1.5-bpw quantization. It highlights progress in on-device LLM inference, though hardware and quantization details need verification.
Community tests full n-gram table for Qwen 3.8 Flash NextA LocalLLaMA user says the SSD-resident PLE (n-gram table) is heavily quantized in common GGUFs, hurting performance. Packing an FP8 PLE with ple_fp8_pack.py reportedly cut errors for an iq3_s model with no speed loss, only extra disk use. The needed safetensors come from Qwen's FP8 repo, and the packed file loads via the --ple-gguf option.
China proposes export controls on 23 AI technologiesOn October 16th, China's commerce and science ministries released a draft revision adding 40 technologies to its export control catalog, 23 of them AI-related, covering foundation/large models, agents and compute clusters; comments run to November 15th and temporary pre-export registration starts immediately. Technologies already made public via open source or academic publication are explicitly exempt, API, app, website or SaaS access is generally not a technology export, and the draft is not a blanket ban on open-weight models.
User Builds Three 4x R9700 Local AI Servers with 384GB Total VRAMTo power AI for his USA Arms business, a user built three servers each with four Radeon AI PRO R9700 GPUs, costing about $10.7k per machine and totaling 384GB VRAM. After hosted models refused certain research tasks, he moved to local AI and chose Qwen for best cost-performance after testing.
Mapping post-Transformer architecture landscape in 2026A community post compares various post-Transformer architectures including Mamba, RWKV, xLSTM, Titans, and others, focusing on core mechanisms, attention variants, and test-time memory. The author suggests the next frontier may shift from scaling alone to getting more intelligence out of limited compute and energy budgets.
Congrats to @lightseekorg on the impressive TokenSpeed results! 🚀 Great to see Qwen3.8-Flash-Next reaching 685 output tokens/s per user on Nvida GPUs. Excited to see the community unlock more of Qwen’s inference potential! [Quoting @lightseekorg]: 685 output…
Someone runs Qwen 27B at ~20 tok/s on a jailbroken PS5 GPUA Reddit r/LocalLLaMA user posted that Qwen 27B runs at roughly 20 tokens per second on a jailbroken PS5 GPU. The post offers few details beyond a link, highlighting an unusual attempt to use console hardware for local LLM inference.
Company Serves 40 People with Local AI on 4x 6000 Pro GPUsA company built a local AI server with 4x RTX 6000 Blackwell MaxQ GPUs, 384GB RAM, and an AMD EPYC CPU to serve 40 people, costing about €55k. Models include DeepSeek V4 Flash, Qwen 3.8 27B, and K2-Horizon 7B.
U-Space offers label-free uncertainty detection for LLMsResearchers released U-Space and its U-Lens, which combine hidden-state doubt directions with mean token entropy to estimate LLM confidence in a single pass, with no labels, resampling, or fine-tuning, and distinguish ambiguity, missing information, and conflicting evidence. It beat multi-pass and supervised baselines on three models and four benchmarks; the only model-specific ingredient is a pre-fitted Anthropic Jacobian lens, with 40+ available.
The A.I. Daily