Welcome Inkling by Thinking Machines Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Back to Articles a]:hidden"> Welcome Inkling by Thinking Machines Published July 15, 2026 Update on GitHub Upvote 4 ben burtenshaw burtenshaw Follow merve merve Follow Pedro Cuenca pcuenq Follow Aritra Roy Gosthipaty ariG23498 Follow What makes Inkling special? Overall Capabilities and Architecture Inference Support Transformers SGLang vLLM Remote Inference with Hugging Face Inference Providers Local Inference with llama.cpp and Unsloth Use Cases Agentic coding with Pi Multi Token Prediction Drafters Multimodal Vision Multimodal Audio Post-training SLURM Scripts Benchmark Results Inkling is a large (1T params!) open model to natively accept image, text, and audio inputs. TLDR; Inkling by Thinking Machines is out on Hugging Face. Inkling is a huge multimodal LLM that understands all modalities (image, audio, text), has agentic capabilities, and supports 1M context. It comes in full BF16 and a well-calibrated NVFP4 variant, and includes speculative MTP layers for faster inference. There’s day-0 support in transformers, SGLang, and llama.cpp.
What makes Inkling special? Inkling is the first large open model with ~1T parameters and 1M context window to natively receive image, text, and audio inputs , trained on 45 trillion tokens of text, images, audio and video. It’s focused on reasoning across modalities such as audio, images, and text; and is intended for domain adaptation via fine-tuning. We’ve tinkered with this model to build some demos and explore the architecture, and we think it’s great for building a new wave of multimodal reasoning apps.
Source: https://huggingface.co/blog/thinkingmachines-inkling
What makes Inkling special? Inkling is the first large open model with ~1T parameters and 1M context window to natively receive image, text, and audio inputs , trained on 45 trillion tokens of text, images, audio and video. It’s focused on reasoning across modalities such as audio, images, and text; and is intended for domain adaptation via fine-tuning. We’ve tinkered with this model to build some demos and explore the architecture, and we think it’s great for building a new wave of multimodal reasoning apps.
Source: https://huggingface.co/blog/thinkingmachines-inkling