Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model
Thinking Machines Lab has released Inkling-Small, an open weights Mixture-of-Experts model with 276B total parameters and 12B active. That is about a quarter the size of Inkling, which carries 975B total and 41B active parameters. The model was trained on NVIDIA GB300 NVL72 systems. It reasons natively over text, images and audio. The context window reaches 1M tokens, and thinking effort is adjustable. Weights ship under Apache 2.0 on Hugging Face. Is it deployable Yes, and the quantized
Thinking Machines Lab has released Inkling-Small, an open weights model with 276 billion total parameters but only 12 billion active parameters at any given time. This matters because the model can run on a single high-end GPU instance, making it accessible to startups and enterprises that cannot afford frontier-scale hardware, while still handling text, images, and audio inputs simultaneously. The model uses a sparse mixture-of-experts architecture where each token routes through only a small subset of its available experts, reducing computational cost compared to using all parameters. On several reasoning and coding benchmarks, the smaller model outperformed its larger counterpart despite being a quarter of its size, though it showed weaker performance on factual recall tasks.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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