Real-Time Hyper-Modal AI for XR/VR/Robotics

Ultra-low latency models with spatial awareness for immersive experiences

Zen LM powers next-generation XR/VR applications and robotic systems with real-time multimodal understanding. From 3D scene generation to spatial audio, from gesture recognition to embodied navigation - our models deliver sub-10ms latency for seamless human-AI interaction in extended reality and physical environments.

Complete AI Stack for Immersive Computing

🧠

Core Language Models

6 models from 0.6B to 1T+ parameters for edge to cloud deployment. Optimized for real-time instruction following and reasoning in XR environments.

👁️

Multimodal Models

10 specialized models for vision, audio, video, 3D generation, and spatial understanding. Built for seamless integration with XR/VR platforms.

🤖

Specialized Systems

Agent frameworks, safety guardrails, embeddings, and IDE tools. Production-ready infrastructure for embodied AI and robotic applications.

High-Performance Infrastructure

Rust-based inference engine with GGUF quantization, training frameworks, and deployment tools for real-time performance.

Why Zen for XR/VR/Robotics?

Real-Time Performance

Sub-10ms latency with optimized quantization and edge deployment. Seamless integration with XR headsets and robotic control systems.

🌐

Spatial Awareness

Native 3D understanding, scene generation, and spatial audio processing. Built for immersive environments and physical world interaction.

🎯

Multimodal Fusion

Unified understanding across vision, language, audio, and 3D. Real-time gesture recognition, voice commands, and environmental awareness.

🔒

Open Source & Transparent

Fully open models, training code, and infrastructure. Complete control for customization, fine-tuning, and deployment on any platform.

Get Started

🤗 HuggingFace

Access all 24+ models via HuggingFace Hub with easy integration

Visit HuggingFace

💻 GitHub

Training code, datasets, documentation, and complete source

View on GitHub

📚 Documentation

Comprehensive guides, papers, and tutorials for all models

Read Docs