NVIDIA Jetson Edge AI Integration
We design model serving, camera ingest, power/thermal planning and field rollout on Jetson Orin/Xavier edge nodes.
Edge AI • On-Premise LLM • MCP
We bring Edge AI and on-premise AI infrastructure into production
We unify NVIDIA Jetson, NPU/GPU clusters, on-premise LLM/SLM, MCP server runtime and sensor/camera pipelines in one architecture—moving pilots to managed production with low-latency inference and data privacy.
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Challenge
Why Edge AI and on-premise LLM projects stall in production
Inference latency: Every frame or token routed to the cloud delays field decisions and breaks real-time edge scenarios.
On-premise data privacy: Sensitive video, audio and enterprise text leaving the premises creates regulatory and security risk.
Edge RTSP/sensor data pipeline: Without standardized camera RTSP, PLC and IoT signals, Vision AI and sensor fusion cannot run reliably.
Pilot-to-production bottleneck: Lab Jetson/GPU setups without production runtime, model pipelines and MCP integration do not scale.
Approach
We build edge compute, on-premise model runtime and operations integration together
Hades Elektronik designs NVIDIA Jetson edge nodes, NPU/GPU compute, on-premise LLM/SLM & MCP servers, RTSP/sensor pipelines and ERP/MES links as one architecture. The goal is not a demo—it is low-latency, observable and secure production AI infrastructure.
Capabilities
Technical capabilities for Edge AI and on-premise AI
On-Premise LLM/SLM & MCP Server Runtime
We run enterprise LLM/SLM models on-prem and connect tools, data sources and agents securely via the MCP protocol.
NPU/GPU Compute Architecture
We size inference capacity from edge NPUs to data-center GPU clusters against latency, batch and cost targets.
Sensor Fusion & Vision Analytics
We fuse RTSP cameras, sensors and industrial signals into vision analytics and decision-support flows.
Model Deployment Pipelines
We build repeatable pipelines from packaging and quantization through edge/cluster rollout and version management.
Use cases
Edge AI and on-premise LLM scenarios
Industrial Quality Control
Low-latency defect detection on the line with cameras + Jetson/NPU, with results flowing into MES/ERP.
Field / Facility Security Analytics
On-prem vision analytics on RTSP camera lines: zone intrusion, PPE, anomaly and event triggering.
Autonomous Systems & Edge Decision
On-site edge decision loops: sensor fusion, local inference and action without cloud dependency.
Enterprise Private LLM / RAG Infrastructure
On-premise LLM/SLM, private document RAG and MCP-based tool access so enterprise AI stays in your data center.
Deployment
From AI pilot to managed Edge AI infrastructure
01
Discovery
Latency targets, data privacy, camera/sensor inventory and existing GPU/Jetson capacity are assessed.
02
Architecture
Edge Jetson, NPU/GPU cluster, LLM/SLM runtime, MCP and integration layers are planned.
03
Deployment
Edge nodes, model serving, RTSP/sensor pipelines and on-prem runtime are set up under control.
04
Integration
AI outputs connect to MCP, APIs, ERP/MES and operational notification systems.
05
Management
Model versions, inference performance, security and capacity are monitored continuously.
Edge AI and on-premise AI FAQ
How do you ensure on-premise LLM data security?
Models and RAG corpora stay on your network. Access control, network segmentation, audit logs and optional air-gapped runtime keep sensitive data off the public cloud.
How do you choose NVIDIA Jetson hardware?
We size Jetson Orin/Xavier class and carrier-board design from camera count, model size, FPS/latency targets, power budget and environmental conditions.
What does MCP protocol integration provide?
MCP lets on-premise LLMs/agents connect to tools, data sources and enterprise systems in a standard, auditable way—reducing custom adapter sprawl.
How do you meet latency performance targets?
Critical inference runs on the edge (Jetson/NPU), optimized with batching and quantization; cloud round-trips are used only when the workload allows.
Does this work with existing cameras and sensors?
Yes. RTSP/ONVIF cameras, PLCs and IoT sensors are inventoried; vision and sensor-fusion pipelines are adapted to your installed base.
- Edge AI
- On-Premise LLM
- Sensor Fusion
Let’s assess your Edge AI and on-premise LLM readiness
Or message us directly on WhatsApp →We will review your Jetson, NPU/GPU, LLM/SLM, MCP and camera/sensor pipeline to create a practical Edge AI infrastructure roadmap.
Service context: AI Infrastructure
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