GPT-OSS, running where the data lives.

Edge AI has left the lab. As generative AI and robotics enter a period of explosive growth, enterprise demand has shifted from experiments to large-scale production — and YUAN has launched a GPT-OSS large language model solution on the NVIDIA Jetson platform to meet it. With the YUAN Pandora toolkit, enterprises deploy fast, keep data off the cloud, and bring multimodal AI interaction directly to the edge.

Built-in memory, built-in confidence

In production, hardware stability decides schedules. With memory shortages and price volatility expected to persist through 2026, YUAN builds on NVIDIA Jetson platforms with validated DRAM integrated directly onto the module. That single design decision removes external memory sourcing and verification from the project plan, simplifies hardware design, and keeps production ramps predictable — a rock-solid foundation for running GPT-OSS at the edge.

What it does in practice

The Jetson series provides optimised runtime inference for leading open-source generative models, and YUAN demonstrates two working applications. A smart access-control voice assistant uses natural language processing to handle visitor registration and enquiries, taking routine load off reception. And visual AI operational analytics pairs YUAN's video capture technology with GPT-OSS to turn raw signals — crowd density, dwell time — into actionable management reports for optimising how space is used.

A seamless path up the range

The Pandora toolkit keeps the barrier to entry low: optimised inference reaches up to 16.2 tokens per second, and deployment is one-stop through either a web UI or the terminal. The hardware path scales with ambition — the cost-effective NVIDIA Jetson Orin Nano as the entry point for edge generative AI, with full compatibility up through Jetson Orin NX and Jetson AGX Orin for heavier computing needs. The aim, in YUAN's words, is to remove the twin frictions of development and supply chain, and make the transition to AI seamless.