EDOM Accelerates Edge AI Deployment with NVIDIA Technologies

Integrating Open-Source Projects and System Optimization to Reduce the Cost of Deploying Large AI Models

July 28, 2026 – Taipei – EDOM Technology (TWSE: 3048), Asia’s best solutions provider, today announced an expanded collaboration with NVIDIA to help enterprises accelerate Edge AI adoption by integrating the NVIDIA Edge AI platform, open-source models, and system optimization technologies. The collaboration enables organizations to lower the barriers to deploying large AI models while reducing overall deployment costs.

As generative AI rapidly evolves from proof-of-concept to real-world applications, enterprise priorities are shifting. Rather than simply pursuing larger AI models, organizations are increasingly focused on running AI reliably on existing hardware platforms, accelerating deployment, and maximizing return on investment. Across industries from smart manufacturing and autonomous robotics to healthcare, Edge AI has become a key driver of digital transformation where efficient deployment is critical to successfully bringing AI projects into production.

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The way enterprises adopt AI is also changing. Instead of building models from scratch, organizations are increasingly leveraging mature foundation models and open-source AI ecosystems to accelerate development, followed by application-specific optimization and deployment. However, as AI models continue to grow in capability, several deployment challenges such as memory capacity, inference performance, and system integration have become major hurdles.

EDOM addresses these challenges by combining the NVIDIA Jetson platform with NVIDIA JetPack, NVIDIA CUDA, NVIDIA TensorRT, NVIDIA Jetson AI Lab, and leading open-source AI models and inference frameworks. The company delivers a comprehensive portfolio of services spanning platform selection, model integration, model quantization, memory optimization, and system validation. This end-to-end approach enables enterprises to maximize hardware resource utilization, reduce deployment costs, and shorten the journey from proof of concept (PoC) to production.

A notable example is NVIDIA Reachy Mini Jetson Assistant, which demonstrates how Headless Mode, NVIDIA Cosmos-Reason2 open VLMs, model quantization, and optimized inference frameworks enable multimodal AI workloads—including vison-language reasoning, Speech-to-Text (STT), and Text-to-Speech (TTS)—to run simultaneously on the NVIDIA Jetson Orin Nano 8GB platform. This showcases how software and system optimization can efficiently execute multiple AI models and reduce memory footprint on resource-constrained edge devices. Similar architectures are increasingly being adopted across smart manufacturing, autonomous robotics, and healthcare applications, supporting use cases such as production line inspection, intelligent vision systems, voice-enabled interaction, and autonomous decision-making. By performing AI inference directly on edge devices, organizations can achieve low-latency performance while enhancing data security and operational efficiency.

“Generative AI has entered the stage of real-world deployment,” said Jeffrey Yu, CEO of EDOM Technology. “today, the biggest challenge for enterprises is no longer finding AI models; it’s successfully deploying AI into products and operational environments. The success of an AI project depends not only on hardware performance, but also on deployment efficiency, system integration capabilities, and overall return on investment. EDOM’s role extends beyond supplying platforms and components. We help customers integrate NVIDIA technologies, open-source AI models, and system optimization strategies to accelerate time-to-market, reduce deployment risks, and enable AI to deliver measurable business value.”

Backed by years of expertise in Edge AI and embedded system integration, EDOM provides end-to-end solutions encompassing selections powered by NVIDIA technologies, open-source AI models integration and application optimization, hardware design, and system deployment. The company has successfully enabled Edge AI applications across smart manufacturing, autonomous robotics, healthcare, smart retail, and smart city initiatives. Looking ahead, EDOM will continue collaborating with NVIDIA and the open-source AI community to help enterprises overcome the challenges of deploying large AI models, accelerate the transition from proof of concept to large-scale deployment, and transform AI innovation into tangible value across the industry value chain.

Reference Case

l   NVIDIA Reachy Mini Jetson Assistant:
https://developer.nvidia.com/blog/maximizing-memory-efficiency-to-run-bigger-models-on-nvidia-jetson/

l   For a comprehensive guide on memory optimization, see this EDOM technical blog: https://www.edomtech.com/en/article-detail/memory-optimization-strategies-for-scaling-ai-models-on-nvidia-jetson/