Inkling

Thinking Machines Lab released Inkling on July 15, 2026, as its first in-house model and the inaugural entry in a planned model family. The official announcement describes Inkling as a Mixture-of-Experts transformer with 975 billion total parameters and 41 billion active per token, pretrained on 45 trillion tokens span Alongside the main release, the announcement previews Inkling-Small, a lighter-weight variant with 12 billion active parameters trained with a similar recipe, aimed at lower cost and latency. The post also introduces an Inkling Playground in the Tinker console for chatting with the model, and demonstrates the model wri

Thinking Machines Lab released Inkling on July 15, 2026, and the model adopts a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion activated per token, according to a 36Kr report. Pre-training used 45 trillion tokens spanning text, images, audio, and video, and the model supports a maximum The same report notes that Inkling's weights were published under an Apache 2.0 license, enabling self-deployment, and that developers can fine-tune the model through Thinking Machines' Tinker platform. The article also describes the architecture as following the DeepSeek-V3 MoE design, with post-training cold-start da

A detailed third-party breakdown corroborates and expands on the official Inkling launch with operational context for teams evaluating self-hosting. It confirms the core spec sheet — 975B total / 41B active MoE, 45T multimodal training tokens, Apache 2.0 license, full weights on Hugging Face — and notes a practical dis The breakdown also covers the controllable thinking-effort dial and the practical infrastructure considerations of self-hosting a model at this scale, useful for developers and platform teams weighing procurement and compliance. It highlights Tinker as the same-day fine-tuning channel, the self-finetuning demonstration

A Hugging Face community blog post (co-published with Thinking Machines) details day-0 developer tooling for Inkling: BF16 and NVFP4 weight variants, speculative multi-token prediction (MTP) layers for faster inference, and first-day integration in transformers, SGLang, vLLM, and llama.cpp. The post frames Inkling as a The same post was later updated to cover the release of Inkling-Small (276B total / 12B active parameters) and the Inkling-Small-NVFP4 variant, plus MXFP8 weight support, one-click deployment on Hugging Face Inference Endpoints (up to 160 TPS for Inkling-Small), and a real-time voice and image demo. A linked collection