Practical Edge AI Inference: Deploying Multi-Framework LLM Inference Environment on Jetson Orin Nano

This article introduces the Rimrock-Runtimes open-source project, designed specifically for the Jetson Orin Nano Super 8GB. It provides benchmarking, configuration, and production deployment solutions for LLM inference environments across multiple frameworks (llama.cpp, ONNX Runtime, MLC-LLM), supports the deployment of Gemma 4 series models and multimodal AI, and helps developers solve framework selection challenges for edge devices.

With the rapid development of LLMs, efficiently running LLMs on edge devices has become a focus. The NVIDIA Jetson Orin Nano Super 8GB balances performance and cost-effectiveness well, but developers face difficulties in choosing among numerous inference frameworks and quantization schemes—thus the Rimrock-Runtimes project was born.

Rimrock-Runtimes is maintained by jsligar and covers mainstream inference frameworks: llama.cpp (lightweight hybrid inference), ONNX Runtime (cross-platform acceleration), and MLC-LLM (compilation optimization). It supports Gemma4 series models (optimized for different parameter scales) and multimodal AI deployment (image + text input processing).

The project's benchmarking evaluation dimensions include: 1. Throughput (tokens per second, affected by batch size); 2. Time to First Token (TTFT, affects interactive experience); 3. Peak memory usage (ensure stability within 8GB memory); 4. Power efficiency (performance per watt, suitable for battery-powered scenarios).

Based on benchmark data, each framework is suitable for different scenarios:

Framework	Best Scenario	Advantages	Notes
llama.cpp	Low-latency interaction	Fast startup, low memory usage	Basic functionality
ONNX Runtime	Enterprise-level deployment	Mature ecosystem and toolchain	Requires model conversion
MLC-LLM	Extreme performance optimization	Full compilation, high throughput	Long compilation time

Developers can choose the appropriate framework based on their scenarios.

Production deployment requires attention to:

1. Containerization: Provide Docker configurations to ensure environment consistency;
2. Model quantization: 4-bit quantization (comparison of GPTQ/AWQ/GGUF) balances accuracy and performance;
3. Thermal stability: Temperature monitoring and frequency throttling protection;
4. Observability: Performance metric collection and error log recording.

Rimrock-Runtimes provides practical engineering references for edge LLM inference and lowers deployment barriers. In the future, with advancements in model compression and inference engines, edge AI applications will become more widespread. The community can contribute by submitting model configurations, optimizing compilation parameters, adapting to hardware, and improving documentation.