Thinking Machines Lab Releases Inkling-Small: A 276B Total, 12B Active Open Weights Multimodal MoE Model

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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 checkpoint is why. Per the model card, the BF16 checkpoint needs at least 600 GB of aggregated VRAM. That is met by 4x NVIDIA B300 or 8x NVIDIA H200. The NVFP4 checkpoint drops that floor to 180 GB. It runs W4A4 on a single B300, which requires SM100+, or W4A16 on two H200s. Supported runtimes are SGLang, vLLM, TokenSpeed, Unsloth and Hugging Face.

That single-GPU path moves a 276B model out of frontier-lab territory. Startups can self-host on one rented B300 instance. Mid-size enterprises with existing H200 capacity can serve it without new hardware. Regulated sectors gain a private-weights option: financial services, healthcare operations, insurance, telecom and public sector. Applicable workloads include coding agents, terminal automation, and document and chart understanding. Audio widens that to call-center analytics, voice interfaces and meeting summarization.

Architecture

Inkling-Small is a 42-layer decoder-only transformer with a sparse MoE feed-forward backbone. Each token routes to 6 of 256 experts, plus 2 shared experts active on every token. Attention is a hybrid of local …

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