Moondream 3 Preview: Frontier-level reasoning at a blazing speed

Pretty good so far. Have 100,000 detections

Tried it's detection out on the playground as a 9B model it's pretty good.

Would be interesting to see how it scores on COCO or Object356 dataset object detection (even if I know will be slower than dedicated object detection model)

The MoE architecture choice here is particularly interesting - the ability to keep only 2B parameters active while maintaining 8B model performance is a game-changer for edge deployment. I've been deploying vision models in production environments where latency is critical, and this sparse activation approach could solve the inference cost problem that's been limiting adoption of larger VLMs. The chart understanding capabilities mentioned look promising for automated document analysis workflows. Has anyone tested the model's consistency across different image qualities or lighting conditions? That's often where smaller models struggle compared to frontier ones.

My understanding is that, while all 8B are loaded into memory, for each token inference step only 2B are selected and used - so tokens are produced faster because there is less computation needed.

Hoping someone will correct me if that's not the right mental model!