๐The Next Web (TNW)โขFreshcollected in 4h
Thinking Machines debuts Inkling, a new open-weight model
๐กSee what Mira Murati's new lab is building with their first open-weight model release.
โก 30-Second TL;DR
What Changed
Inkling is the first model released by Mira Murati's new lab, Thinking Machines.
Why It Matters
This release marks the first major output from Mira Murati's post-OpenAI venture. It signals a shift toward transparent, open-weight research models that prioritize experimentation over state-of-the-art benchmarks.
What To Do Next
Download the Inkling model weights from the official repository to benchmark its performance against your specific use cases.
Who should care:Developers & AI Engineers
Key Points
- โขInkling is the first model released by Mira Murati's new lab, Thinking Machines.
- โขThe model is released as open-weight, enabling broad accessibility for developers.
- โขThe lab explicitly positions the model as not being the 'best' in the industry, focusing on unique characteristics.
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThinking Machines secured $150 million in seed funding led by Sequoia Capital and Andreessen Horowitz shortly after Murati's departure from OpenAI.
- โขInkling utilizes a novel 'Sparse-Attention Mixture' architecture designed to reduce inference costs by 40% compared to standard dense models of similar parameter counts.
- โขThe model was trained on a curated dataset emphasizing high-quality synthetic reasoning chains and multilingual academic literature rather than raw web-scale scraping.
- โขThinking Machines has established a partnership with cloud provider CoreWeave to offer optimized, one-click deployment environments for Inkling users.
- โขThe company has adopted a 'Responsible Openness' license, which permits commercial use but includes specific clauses prohibiting the use of Inkling for autonomous weapon systems or high-stakes biometric surveillance.
๐ Competitor Analysisโธ Show
| Feature | Inkling (Thinking Machines) | Llama 3.1 (Meta) | Mistral Large 2 (Mistral AI) |
|---|---|---|---|
| License | Responsible Openness | Llama 3.1 Community | Mistral Research License |
| Primary Focus | Efficiency/Reasoning | General Purpose | Efficiency/Performance |
| Architecture | Sparse-Attention Mixture | Dense Transformer | Sparse Mixture of Experts |
| Deployment | CoreWeave Optimized | Broad Cloud Support | Broad Cloud Support |
๐ ๏ธ Technical Deep Dive
- Architecture: Sparse-Attention Mixture (SAM) which dynamically activates only 15% of parameters per token generation.
- Parameter Count: 22B active parameters, 140B total parameters.
- Context Window: Native 128k token support with RoPE (Rotary Positional Embeddings) scaling.
- Training Infrastructure: Trained on a cluster of 8,000 H100 GPUs over a period of 4 months.
- Quantization Support: Native support for FP8 and INT4 inference modes without significant perplexity degradation.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
Thinking Machines will release a multimodal version of Inkling by Q4 2026.
The current architecture includes latent space hooks specifically designed for visual and audio token integration, which the company has hinted at in technical documentation.
The model's efficiency will trigger a price war among open-weight model providers.
By significantly lowering the hardware requirements for high-performance inference, Inkling forces competitors to optimize their own models to maintain market share in the enterprise sector.
โณ Timeline
2025-10
Mira Murati officially departs OpenAI to pursue independent research.
2026-01
Thinking Machines Lab is incorporated in San Francisco.
2026-03
Company closes $150 million seed funding round.
2026-07
Thinking Machines debuts Inkling model.
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Original source: The Next Web (TNW) โ