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Qwen releases Qwen3.8-27B, an open-weights 27B dense vision-language model with major gains in coding, professional work, and long-horizon agentic tasks, available in FP8 with flexible thinking control.
Speculation about the upcoming Qwen 3.8 release, questioning whether it will be a dense 27B model or a MoE variant like the previous 35B-A3B, with discussion of performance implications for local hardware.
Qwen3.8-27B is a new AI model with enhanced capabilities in coding, agentic tasks, and vision-language understanding, offering flexible thinking control and long context lengths. It is available on Hugging Face and designed for deployment-friendly use.
Bonsai 27B is a 1-bit dense large language model that can run locally in a browser using custom WebGPU kernels, enabling efficient on-device inference.
A user shares their experience comparing several large language models (Qwen, Gemma) on complex tool-calling tasks, finding Qwen3.5 122B the most reliable, while criticizing smaller MoE models for instability.
Discusses the largest dense model that can be loaded in 128 GB RAM using MXFP4 quantization.
Gemma 4 12B QAT (dense) achieves over 1000 tokens per second prefill on an 8GB RTX 4060 with 120k context using TurboQuant, enabling full GPU layer offloading. This represents a 42% increase in prefill speed over previous methods.
User runs Gemma 4 31B dense model on 8GB VRAM gaming laptop at ~3 tokens/sec using llama.cpp with MTP speculative decoding, demonstrating feasibility of running a 31B dense model on consumer hardware and proposing agentic workflows where a fast MoE model routes to this slower dense model for hard tasks.
Google DeepMind researcher announces the release of Gemma 4 12B, a dense encoder-free model that processes text, image, and audio inputs, continuing work on unifying models across modalities.
Google released Gemma 4 12B, a dense mid-sized model that runs on laptops, under Apache 2.0, now available in LM Studio.
A new 3.6-27B release shows MoE closing the performance gap with dense models, especially in coding tasks and large context windows, though dense still leads overall.
Qwen releases Qwen3.6-27B, a 27B dense model claiming flagship-level coding performance surpassing the larger Qwen3.5-397B-A17B MoE, with impressive SVG generation demos.
Alibaba releases Qwen3.6-27B, a 27-billion-parameter dense model delivering flagship-level coding performance.