{
  "id": 18572853,
  "title": "Thinking Machines debuts Inkling 2 Small amid reported $5B fundraising push",
  "url": "https://urgent.news/2026/11/06/thinking-machines-debuts-inkling-2-small-amid-reported-5b-fundraising",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-11-06T21:27:42.000Z",
  "source": {
    "name": "SiliconANGLE",
    "slug": "siliconangle",
    "url": "https://siliconangle.com/2026/11/06/thinking-machines-debuts-inkling-2-small-amid-reported-5b-fundraising-push/"
  },
  "original_language": "en",
  "account": "Thinking Machines Labs Inc. unveiled Inkling 2 Small, a new large language model capable of automating AI training tasks, on Thursday. The company, founded last year by former OpenAI Group PBC Chief Technology Officer Mira Murati, is reportedly raising $5 billion in funding, with Business Insider citing Accel as a major investor. Inkling 2 Small was trained using a cluster of GB300 NVL72 racks, with each system comprising 72 Blackwell Ultra graphics cards and half as many central processing units.\n\nThe model was pre-trained in just 68 hours and refined through a nine-day reinforcement learning workflow, incorporating numerous intermediate training steps. Inkling 2 Small honed its programming capabilities by completing thousands of complex coding challenges, including \"autoresearch\" exercises that involved post-training neural networks, optimizing kernel components, and conducting AI research tasks.\n\nThe company tested the model's machine learning skills by asking Inkling 2 Small to fine-tune Qwen3.5-4B, a popular open-source LLM, to improve its weather prediction capabilities. According to Thinking Machines, the model doubled the forecast score in just 4.5 hours. Additionally, Inkling 2 Small excels at automating business tasks, outperforming Spark Open, Kimi K3, and other open-source models on three knowledge work benchmarks and several financial task datasets.\n\nThe LLM features a vision encoder that analyzes images, enabling it to visually verify the quality of generated graphics. If a user requests the creation of an e-commerce store, Inkling 2 Small can not only check the website's code for bugs but also visually inspect product pages to ensure they render correctly. The vision encoder constitutes 292 million of the model's 594 billion parameters, with about 17 billion parameters becoming active when a user inputs a prompt.\n\nInking 2 Small's adaptability allows developers to trade off output quality for cost-efficiency by adjusting the amount of computing power spent on tasks. Users can further extend the model's capabilities through post-training, or customization, using their own data. Thinking Machines claims Inkling 2 Small learns faster than its previous models, promising more cost-efficient post-training runs. The model will be available via Thinking Machines' Tinker cloud service upon launch, with the company planning to release the model's weights under an open-source license the following week.",
  "summary": "Thinking Machines Labs Inc. has released a new large language model that can automate artificial intelligence training tasks. The algorithm, Inkling 2 Small, made its debut late Thursday. Around the same time, Business Insider reported that Thinking Machine is in the process of raising a $5 billion funding round led by Accel. The company, which […] The post Thinking Machines debuts Inkling 2…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Techmeme",
        "title": "Thinking Machines Lab debuts Inkling 2 Small, a MoE model with 594B total and 17B active parameters, and plans to release weights next week after safety testing (Thinking Machines Lab)",
        "url": "https://urgent.news/2026/11/06/thinking-machines-lab-debuts-inkling-2-small-a-moe-model-with-594b",
        "published": "2026-11-06T00:15:00.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}