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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.