MindGrid is designed to operate across edge devices and cloud environments, providing flexible deployment, real-time responsiveness, and scalable compute resources. Our hybrid architecture ensures that intelligence can run locally on a robot for latency-sensitive operations while leveraging the cloud for high-performance perception, advanced reasoning, and speech synthesis.
Edge Deployment
Lightweight OSS Models
Core perception (smolVLM), reasoning (LLM beta), speech (TTS/STT) can run on embedded devices like Jetson, x86 SBCs, or edge GPUs.
Low-Latency Control Loops
Critical tasks such as navigation, grasping, and collision avoidance are executed locally to ensure immediate responsiveness.
Privacy & Autonomy
Data stays on-device when required; sensitive streams never leave the robot unless explicitly routed through secure channels.
Adaptive Compute
Dynamically adjust model size or batch frames to meet hardware constraints and real-time requirements.
Cloud / Hybrid Deployment
Premium API Offload
Heavy perception models (Vision+), multi-agent reasoning (LLM+), and high-fidelity TTS+ can be processed in the cloud or private cloud instances.
High Scalability
Multiple robots can share compute resources, allowing organizations to deploy fleets without replicating infrastructure.
Persistent Memory & Coordination
Cloud components store maps, state histories, and multi-agent coordination data for long-horizon tasks.
Monitoring & Observability
Full audit logs, performance metrics, and telemetry streams support enterprise-grade reliability and compliance.
Gateway Layer
Unified Access
All OSS and Premium modules are accessed through a single gateway, simplifying API calls and deployment pipelines.
