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LatestThinkingmachinesInkling
ThinkingmachinesAny-to-Any

Thinking Machines Lab debuts Inkling, its first open model

The lab's inaugural open-weights release is a mixture-of-experts system that takes image and audio inputs, shipped under a permissive Apache 2.0 license.

Jul 15, 2026
Major releaseApache 2.0
Inkling

Thinking Machines Lab has released Inkling, described as its first open-weights model and a mixture-of-experts (MoE) multimodal system that accepts image and audio inputs alongside text. The model is published under the permissive Apache 2.0 license, according to the lab's announcement.

The MoE design is notable because it activates only a subset of the model's parameters for any given input, a routing approach that lets teams scale total capacity while keeping the compute cost of each forward pass in check. Pairing that architecture with native image and audio handling puts Inkling in the growing category of open models built to reason across more than one input type.

Why it matters

An Apache 2.0 license is one of the least restrictive terms an AI lab can choose, which means developers can study, fine-tune, and deploy Inkling commercially without the usage caveats attached to many so-called open releases.

  • Open weights: the model parameters are available for download and self-hosting.
  • Multimodal inputs: image and audio are supported in addition to text.
  • MoE architecture: sparse expert routing aimed at efficiency at scale.

As an initial release, Inkling establishes a baseline rather than iterating on a prior version. The full picture — including parameter counts, context length, and benchmark results — will come into focus as the community begins testing the weights against comparable open multimodal systems.

Sources

  • Inkling: Our Open-Weights Model

    Hacker News

    Visit
  • thinkingmachines/Inkling

    Hugging Face

    Visit
  • Welcome Inkling by Thinking Machines

    Announcement

    Visit
OlderCrisperWhisper 2.0 Large targets verbatim transcriptionNyralabs · Speech → Text · 4 months agoNewerinclusionAI ships LLaDA2.2-flash diffusion LLMinclusionAI · Text / LLM · 4 months ago

Get the model

Hugging FaceHacker NewsAnnouncement

Specs

Context window1.0M tokens
Size1.9 TB
PrecisionBF16
ArchitectureInklingForConditionalGeneration
LicenseAPACHE-2.0
Downloads2.1M
Likes1.8K

Can you run it?

Datacenter only — about 574.4 GB even at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    1.9 TB
  • 8-bit

    Multi-node cluster

    1.0 TB
  • 4-bit

    8×H100 80 GB node

    574.4 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyVision-LanguageText / LLM
2 versions — view changelog

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