Thinking Machines Lab Drops Its First Model
Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.
Get smart on it
Thinking Machines Lab, a startup founded by former executives and researchers from OpenAI, has released its first artificial intelligence model called Inkling. The model is open-weight, meaning researchers and startups can download and modify it, and was trained to process audio, video, and text input with 975 billion parameters. The release matters because open-weight models are cheaper to run and easier to customize than closed models, and this could help Thinking Machines compete with established AI companies while advancing the company's stated vision that AI technology should be decentralized rather than controlled by a few companies. The startup's release demonstrates how AI defectors from major companies are establishing themselves as significant competitors in the rapidly developing AI industry.
Related news
NVIDIA AI Releases Nemotron 3 Embed: An Open Embedding Collection Whose 8B Checkpoint Ranks #1 on RTEB
Embedding models decide which passages an agent ever sees. NVIDIA released Nemotron 3 Embed model to work on that layer. It targets production-scale RAG, agentic retrieval, code retrieval, and agent memory. What is Nemotron 3 Embed? The model collection includes three open checkpoints. Nemotron-3-Embed-8B-BF16 is the accuracy-first option. Nemotron-3-Embed-1B-BF16 carries the same design into a smaller footprint. Nemotron-3-Embed-1B-NVFP4 is the Blackwell-optimized 4-bit path. All thre
Moonshot AI Releases Kimi K3: A 2.8 Trillion Parameter Open MoE Model With Kimi Delta Attention and 1M Context
Moonshot AI just released Kimi K3. It is a 2.8-trillion-parameter model with native vision and a 1-million-token context window. Moonshot calls it the world’s first open 3T-class model. What is Kimi K3? Kimi K3 is a sparse Mixture-of-Experts (MoE) model built on two architectural updates. Those are Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). Both change how information flows across sequence length and model depth. K3 targets long-horizon coding, knowledge work, an
