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Thinking Machines Lab releases Inkling, a 975B-parameter open-weights multimodal MoE model with 41B active parameters and a controllable thinking effort feature. It uses an encoder-free approach for multimodality and achieves state-of-the-art on several benchmarks.
Researchers trained a vision-language model without a vision encoder for only $100, inspired by Gemma 4 12B, achieving a 30% reduction in end-to-end latency on an M3 Pro MacBook.
Discusses leveraging Gemma 4 12B's encoder-free architecture for native voice input, seeking out-of-the-box solutions for low-latency streaming audio ingestion.
Google DeepMind announces Gemma 4 12B, a novel encoder-free multimodal AI model that integrates vision and audio directly into the LLM backbone, delivering advanced reasoning and agentic capabilities on laptops with 16GB of RAM, released under Apache 2.0 license.
Google's Gemma 4 12B introduces an encoder-free multimodal architecture that competes with larger models, though benchmark comparisons show it trailing Qwen 2.5 9B on most tasks. The article also covers related developments including open-weight model security risks, Uber's Claude Code spending caps, and NeurIPS's misuse of an uncalibrated AI detector.
Google's new Gemma 4 12B is a single decoder-only transformer with encoder-free multimodal input, achieving strong benchmarks while being small enough to run locally on a budget GPU. It is released under Apache 2.0 license.
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's Gemma 4 12B model enables local multimodal AI using an encoder-free architecture.
Google launches Gemma 4 12B, an encoder-free multimodal model with native audio support, optimized for local execution on laptops under Apache 2.0.