Welcome Inkling by Thinking Machines
We're on a journey to advance and democratize artificial intelligence through open source and open science. Back to Articles a]:hidden"> Welcome Inkling by Thinking Machines Published July 15, 2026 Update on GitHub Upvote 8 +2 ben burtenshaw burtenshaw Follow merve merve Follow Pedro Cuenca pcuenq Follow Aritra Roy Gosthipaty ariG23498 Follow Inkling is a large (1T params!) open model to natively accept image, text, and audio inputs.
Key Takeaways
- 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.
- Overall Capabilities and Architecture Inkling is a decoder-only multimodal Mixture-of-Experts model with 975B total and 41B active parameters.
There are a lot of things going on, so let's break each part down: Decoder-only: This means that the architecture supports causal autoregressive generation, like in most state-of-the-art LLMs.
- Each attention layer learns position directly in the attention logits.
Aside from key-query-values, there's a fourth projection producing a per-token, per-head relative feature R.
- Short convolution: The model uses a distinctive short 1D convolution, or over the hidden states.
SConv reads the current token and the previous hidden states, with being the sliding window size.
- The multimodal towers are relatively simple modules, unlike other models that employ separate encoders for each modality.
Stats & Key Facts
- #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.
- #The model has 256 experts, as we'll see later.
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. 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. Overall Capabilities and Architecture Inkling is a decoder-only multimodal Mixture-of-Experts model with 975B total and 41B active parameters. There are a lot of things going on, so let's break each part down: Decoder-only: This means that the architecture supports causal autoregressive generation, like in most state-of-the-art LLMs.
For more details please read the original article at Hugging Face.
Continue Learning
Comments
Comments appear only after moderation. Your email identifies your submission to the moderator and is never displayed here.
No approved comments yet.