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Deploy generative AI and run large language models (LLMs) locally with scalable edge computing platforms — enabling private, low-latency inference while eliminating cloud dependency, latency, and recurring costs.
Cloud LLMs are powerful for experimentation — but edge LLMs are built for real-world deployment. For many industrial and enterprise applications, cloud AI introduces latency, data exposure risks, and unpredictable costs.
Sensitive data remains on‑premise without sending it to external cloud services.
Run AI models even in environments with limited or no connectivity.
Edge inference enables real‑time responses for AI assistants and automation.
Local inference reduces ongoing cloud GPU usage and operational cost.
Modern AI deployment is shifting toward a hybrid model:
◆ Cloud for training and orchestration
◆ Edge for real-time inference and execution
Geniatech enables this shift with modular, on-prem edge AI platforms built for scalable LLM deployment in real-world environments.
Geniatech provides a full portfolio of edge AI hardware optimized for local LLM inference — from ready-to-deploy systems to customizable embedded platforms.
Compact, fanless, industrial-grade AI systems designed for real-time LLM inference.
Enhance existing platforms with dedicated AI acceleration for LLM and hybrid workloads.
Flexible embedded platforms for developing tailored edge AI and LLM solutions.
Geniatech offers a different approach:
