On-Device LLM Inference on NVIDIA Jetson Orin Nano

This project demonstrates the power of on-device generative AI by deploying a fully containerized LLM inference system on the NVIDIA Jetson Orin Nano. It enables real-time text generation, analytics, and monitoring – all running locally without any cloud dependency. The setup showcases how edge AI can deliver powerful, privacy-centric, and ultra-efficient model inference experiences.

Use Case 

As enterprises move toward decentralized AI infrastructure, there’s a growing need for private, low-latency generative model inference at the edge.
This system is designed for: 

• Edge-based document understanding and assistant applications 

• Autonomous devices needing on-the-fly model response generation 

• Offline inference environments with strict data privacy constraints

Challenge 

• Low-latency inference: Applications like autonomous systems or AR assistants require sub-10ms responses. Cloud-based APIs add unacceptable round-trip delays. 

• Data privacy: Sensitive data cannot be sent to cloud servers without risking exposure or regulatory violations. 

• Offline operation: Many industrial or defense scenarios require AI systems that can operate with limited or no internet connectivity.
• Integration complexity: Traditional LLM deployments assume high-power servers; embedded devices need optimized and containerized frameworks.

Solution 

The solution uses the Jetson Orin Nano’s Ampere GPU and multicore CPU with a fully local LLM stack packaged via Docker. When the container is launched, it automatically starts with an Ollama server that initializes a quantized LLM on the GPU. The llama.cpp backend performs tensor computation for efficient 4-bit inference. 

A custom web-based UI enables text query input and displays generated responses with live performance metrics. Because all computation happens locally, the system achieves ultra-low latency and ensures that no sensitive data leaves the device. 

This approach demonstrates a complete, reproducible, and portable edge AI environment that runs entirely offline and supports deployment at scale.

Solution Architecture 

• Jetson Orin Nano (8GB) as the edge AI deployment hardware
• Ollama as the lightweight model serving layer with Jetson-native support
• llama.cpp as the optimized backend for 4-bit quantized inference
• Docker for environment packaging, GPU support, and deployment reproducibility
• Custom UI Dashboard for prompt submission, performance analytics, and monitoring

UI Demonstration: Jetson Orin Nano On-Device LLM Console

• Model Selection Interface : The UI shows Jetson Orin Nano hardware specs and lets the user choose between available quantized models inside the local Ollama container.
• SmolLM Model Running Locally : The chat interface displays SmolLM responding to a user query along with visible token statistics such as prompt tokens, output tokens, and tokens-per-second.
• Gemma Model Generating a Reply : Gemma2 is selected as the active model and is generating a response entirely on-device, reflected by the “Thinking…” status in the console.

Data Flow Layers 

• Users interact with the local UI. 
• Request is routed to the Ollama server running inside the container. 
• Ollama invokes the llama.cpp backend to process the model inference on the GPU. 
• The generated output tokens stream back to the UI in real time. 
• The dashboard displays latency, throughput, and GPU metrics for observability. 

Step-by-Step Solution Flow 

• Container Initialization – Docker loads the pre-configured runtime with CUDA, Ollama, and llama.cpp. 
• Model Loading – Ollama initializes and loads the quantized model into GPU memory. 
• User Query Input – The user submits a query through the local UI. 
• Inference Execution – llama.cpp performs high-speed inference on-device for real-time generation. 
• Performance Monitoring – The dashboard visualizes inference latency and GPU utilization. 
• Output Delivery – Generated results stream back instantly no internet required. 

Data Sources Supported 

• Local text and document repositories 

• Embedded SQL/NoSQL databases 

• On-device RAG or vector knowledge bases 

• Real-time IoT or sensor data streams 

System-Level Technical Advantages 

• Ultra-low latency: On-device inference eliminates network delays. 

• Privacy-first design: Sensitive data remains entirely within local hardware. 

• Offline reliability: Operates without internet access – ideal for remote or secure sites. 

• Portable deployment: Docker ensures reproducible, version-controlled environments. 

• Real-time observability: Integrated dashboard visualizes GPU load and inference speed. 

• Modular scalability: Identical containers can be deployed across multiple Jetson devices. 

Key Outcomes of On Device LLM Inference on NVIDIA Jetson Orin Nano

• End-to-end on-device proof-of-concept: Demonstrated full LLM inference and analytics on Jetson Orin Nano. 

• High throughput: Quantized 1B–3B models deliver ~28–55 tokens/sec with stable GPU utilization. 

• Sub-second latency: Verified through live dashboard metrics under continuous load. 

• Fully self-contained: Inference, UI, and monitoring stack run 100% locally.
• Reproducible delivery: Docker image supports easy updates and enterprise rollout.

Benefits for Enterprises 

This project highlights how modern LLMs can run efficiently on compact edge hardware like the Jetson Orin Nano, enabling private, low-latency inference without cloud dependence. By combining Ollama, llama.cpp, and containerized deployment, the system delivers a reproducible and fully offline generative AI environment. The real-time UI dashboards further demonstrate practical usability for edge applications. This approach proves that on-device LLM inference is both feasible and production-ready for a wide range of distributed AI workloads.