We optimize vision, language, and action models for real-time edge deployment. Every model here is compressed, benchmarked, and ready for production robotics.
The robotics community deserves production-ready models, not just research checkpoints. We take the best open foundation models — CLIP, SAM2, DINOv2, Qwen, Depth Anything — and make them actually deployable on the hardware robots use: Jetson Orin, industrial PCs, edge GPUs.
Every model is quantized (INT4/INT8), exported (ONNX/SafeTensors/TorchScript), and benchmarked on real hardware. No guesswork, no "should work in theory" — measured performance on real silicon.
Organized by capability for the ANIMA robotics stack.
Segmentation, features, depth estimation, and visual grounding for robotic scene understanding.
INT4 quantized language models for instruction following, planning, and robotic reasoning.
Vision-language models for visual QA, scene description, and grounding language to observations.
Vision-Language-Action models for end-to-end robotic control and manipulation.
Our 4-stage pipeline takes any 7B+ VLA model down to <2GB for real-time edge deployment.
Automated hyperparameter optimization via Optuna · 400+ trials across 4 GPUs · W&B experiment tracking