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.
Alibaba Details QwenGyre to Accelerate Extreme-Horizon Agent TrainingAlibaba's Qwen team, with USTC and Tsinghua, released QwenGyre, an elastic RL framework for extreme-long-horizon agent training. An Elastic Scheduler shifts GPUs between rollout generation and training to cut idling and trajectory redundancy; single rollouts often consume 700,000 to about 1 million tokens.
Decision 3.0 open multimodal decision models releasedThe vLLM Semantic Router team released Decision 3.0 (d3), a family of decision models from 0.6B to 27B under Apache-2.0 that read text, images, and video. It ranks #1 on both Jev Decision Index boards.
Users ask if local models can reverse engineer softwareA Reddit user asked whether local models like Qwen 27B can reverse engineer games and programs, and whether a fine-tuned model exists for this. The discussion reflects community interest in using local models for reverse engineering.
Jebadiah v2.1 open models score labels from logitsA developer released Jebadiah v2.1 27B and 9B open weights that treat decisions as closed-set scoring, reading option probabilities from candidate-label logits with no generation step. On the Decision Index 0.3 public suite, 27B scored 57.03 and 9B 47.20, both above v2, though tool-use regressed with When2Call down 6.38 and 7.44. Weights use Apache-2.0 on a Qwen base.
Qwen 3.8 Flash runs at ~21 tok/s on RTX 3060A developer reports running a 68GB quantized Qwen 3.8 Flash Next MoE at about 21 tok/s on an RTX 3060 12GB with 16GB RAM, using expert-routing prediction to speed CPU/GPU offload with no gate pruning and bit-exact output. Warm cache reaches 24+ tok/s, versus roughly 1.4-2.1 tok/s with stock llama.cpp.
Qwen-Image-2.1-Turbo Lands on Hugging FaceA Hugging Face model page for Qwen/Qwen-Image-2.1-Turbo appeared, with the LocalLLaMA community sharing the link, indicating a new release of Alibaba's Qwen image model.
Qwen Releases Open-Weights Qwen-Image-2.1-TurboQwen released Qwen-Image-2.1-Turbo, a 7B visual generation checkpoint that produces 2K images in just 8 denoising steps and supports natural-language edits, with open weights and Diffusers support.
User Says Qwen Flash Next Lags in Long-Term Agent TasksA Reddit user reports that Qwen Flash Next at iq4_xs follows instructions less reliably and hallucinates more than Qwen 27B while using far fewer tokens. It excels at one-shot and benchmark tasks, and did not loop on Strata, but felt weaker for long-term agentic assistant work.
Users question why Qwen 27B outperforms GPT-4oA r/LocalLLaMA post asks why Qwen 27B performs so well while the trillion-parameter GPT-4o is worse. The poster speculates about higher-quality pretraining data or new techniques.
Reflection AI to release US open-weight model to rival DeepSeek and QwenAxios reports that Reflection AI is about to release a US open-weight model aimed at competing with Chinese open models DeepSeek and Qwen. Community discussion hopes it stays under 200B parameters for memory-constrained users and expects more Western open models to follow.
Qwen model reportedly hallucinated signed Alibaba Cloud URLA user running Qwen3.8-Flash-Next locally on a Mac Studio saw the model generate a signed URL pointing to an Alibaba Cloud OSS bucket during browser tool calls for Amazon product research, and stopped the session. The user found a similar report from 45 days earlier and said it may be a harmless hallucination from Alibaba coding traces in training data, though data exfiltration cannot be ruled out.
Developer posts update on local Yandex 80B finetuneA developer on Reddit shared progress finetuning Yandex's AliceAI-80B-A3B base model. After the first SFT round the model showed chain-of-thought reasoning and conversation ability but was underfit from insufficient instruct data, so the alpha checkpoint won't be released. Next, roughly 5M more general instruct tokens will be added, training locally on three 32GB V100s.
From one 3090 to 20 DGX Sparks for local LLMsA user on r/LocalLLaMA recounts scaling local LLM hardware from a single RTX 3090 to 16-GPU rigs and finally multiple ASUS GB10 (DGX Spark) nodes, running a 397B model in FP8 with 20% speedup and posting the first 8-node Spark setup on NVIDIA's forum.
Community asks if Qwen3.8 Flash Next work speeds up Qwen4A Reddit user asks whether optimization work on Qwen3.8 Flash Next will carry over to a faster Qwen4 release, citing the official Qwen blog post and hoping the effort pays off when the next model drops.
Blogger says self-hosting AI saves no money but sticks with itA r/LocalLLaMA user shared a blog post arguing that self-hosting AI is not cheaper than using APIs, but still prefers it for reasons such as data privacy. The post sparked debate over the value of local deployment.
Cloudflare's Clef model removes humans from AI agent loopCloudflare launched Clef and Clef-flash, challenging TypeSafe AI's Jev decision model. Clef-flash delivers classifications in about 39 milliseconds, over ten times faster than Jev. Both models are built on Apache 2.0-licensed Qwen and let AI agents make structured decisions without generating text.
NVIDIA DGX Spark 64GB Gives Developers More Ways to Build and Scale Local AINVIDIA will offer a 64GB unified-memory DGX Spark this month through Acer, ASUS, Dell, Gigabyte, HP and MSI, keeping the GB10 Grace Blackwell Superchip, DGX OS and full AI software stack, and supporting up to 100-billion-parameter models on device. Two units can cluster via Sync Cluster Assistant, delivering up to 1.7x performance on NVIDIA's Qwen 3.8 27B test.
286 PC runs full chat and image generationA developer got a 1980s Tandy 1000 to do chat and text-to-image generation by linking over WiFi to a PC server running Qwen and Krea 2, producing a thumbnail in about 9 seconds; the code is GPLv3.
Local AI costs €0.12/hour: is paying for frontier models cheaper?A LocalLLaMA user says running Qwen locally on an AMD 7900 XTX plus 9800X3D costs about €0.12 per hour, based on a local electricity price of €0.25/kWh. The user asks whether frontier models are actually cheaper per million tokens, or whether the calculation is wrong.
Qwen3.8-27B is now accessible via @nebiustf. Whether you are building agents or doing deep research, this 27B dense model is ready for your multi-step workflows! 🥳 [Quoting @nebiustf]: Qwen3.8-27B is now live on Nebius Token Factory. A compact 27B dense mode…
Qwen-family LLMs becoming backbone of audio modelsA community survey of audio model architectures finds 32 audio model families use Qwen-family backbones, 20 of them Qwen3, spanning TTS, ASR, music generation and speech-to-speech.
User tests qwen3.8-flash-next on 4x R9700, beats expectationsA Reddit user shared results running qwen3.8-flash-next on four AMD R9700 GPUs with an MXFP4-FP8 quantized build for agentic coding tasks. Speed and quality exceeded expectations: single-stream generation above 150 tokens/s and prefill above 10,000 tokens/s.
Holo4 launched: generalist computer-use agent modelsH Company released the Holo4 series of agentic models in 27B dense and 35B-A3B MoE sizes, plus Holotron4 Nano. They operate software via GUIs, code, MCP and APIs. On OSWorld 2.0, Holo4 27B scores 61.7% and 35B-A3B 30.9%, far below Opus 5.5's 81.8%, but at much lower parameter count and cost.
Qwen agent plays World of WarcraftA developer set up a private World of Warcraft server and a browser client, then built a custom MCP so LLM agents such as Qwen can play the game. The setup uses no visual input and recommends models outputting over 50 tokens per second.
Logit penalty on Qwen models improves math accuracyA user tested logit-bias penalties on words like "wait", "maybe" and "perhaps" across quantizations of Qwen3.5-4B in llama.cpp, running 50 random MATH-500 questions, building on a Meta paper on overthinking markers. The paper had not covered llama.cpp's quantization options.
KoboldCpp ships built-in agent harnessKoboldCpp now ships a built-in Agent Harness, enabled with a single checkbox and positioned as a lightweight alternative to Opencode, Codex, and Claude Code. It has 9 built-in tools and a 2k-token system prompt, supports MCP, AGENTS.md, and context compaction, and can connect to any OpenAI Chat Completions-compatible endpoint. Recommended: 28k context, 8k generation, 12GB VRAM.
Swift 1.5 27b released: Swift Qwen gets fasterA community user released Swift 1.5 27b, claiming Swift Qwen is faster. The post offers no specific benchmarks or technical details, only enthusiasm for the update.
UkisAI Releases Swift Family of Efficient Reasoning ModelsUkisAI launched the Swift family of reasoning LLMs built on Qwen, cutting thinking tokens by penalizing pathological overthinking and restoring accuracy via RL. Swift Flash Next reduces thinking tokens 63.4% with 1.8x speedup; Swift1.5 27B cuts 58.5% with slightly higher scores; Swift Bonsai 2 cuts 39.8%. API, demos, and quants are provided.
Benchmark: ThinkingCap vs Swift vs Qwen 3.8-27BA third-party Aider evaluation found that ThinkingCap and Swift fine-tunes cut Qwen3.8-27B's median reasoning tokens from 12,547 to about 7,400, a roughly 40% reduction matching their model cards, with first-try pass rates of 27.1%-30.8% and retry pass rates of 75.7%-77.6%, showing no meaningful performance loss.
Alibaba launches Qwen Audio 3.1, cuts pricesAlibaba's Qwen team released Qwen-Audio-3.1, five models for speech recognition, text-to-speech and real-time interaction. ASR improves multilingual and dialect recognition and cleans filler words; ASR-Next adds multi-speaker ID, timestamps and emotion/ambient detection, with AI audio prices cut up to 95%.
The A.I. Daily