You’re about to see something you probably haven’t seen anywhere else. 👀 In this demo, I build an AI agent on an Intel Lunar Lake device, export it as a zip file, and run that exact same agent instantly on a Qualcomm Snapdragon X Elite device — no rebuilds, no changes, no cloud, and no headaches. Then I flip it. I build a completely different agent on Qualcomm (including vision + emotion + chat models) and run it back on Intel just as easily. All of this is done locally in each device, inside Model HQ. From an enterprise IT perspective, this demo solves for a problem that’s traditionally very hard to solve. Qualcomm Snapdragon X Elite systems run on Arm architecture, while Intel Lunar Lake systems run on x86. In this demo, the same AI agent is built once and deployed unchanged across both Arm and x86 devices, entirely on-device, with no cloud dependencies. For IT teams managing mixed hardware fleets, this approach simplifies deployment, governance, security reviews, and long-term maintenance, while avoiding platform-specific forks or vendor lock-in. What this video shows: 🔁 Build once, share everywhere — Intel ↔ Qualcomm 📦 Agents exported and shared as simple zip files ⚙️ No cloud calls, no re-compiling, agents run across mixed device fleet 🤖 Text + RAG agents and multi-model vision agents 🧠 Multiple models working together inside one agent 🔒 Fully private, on-device AI Model HQ turns every AI PC into a portable AI workstation, letting you build agents, workflows, and bots once — and run them across different hardware platforms with ease. If you’re managing mixed hardware fleets, building enterprise AI workflows, or just want real portability for on-device AI, this demo is for you. 👉 Watch to see how fast it really is to build, export, and run AI agents across devices. Visit LLMware.ai for more information. #OnDeviceAI #ModelHQ #AIAgents #NoCodeAI #EdgeAI #AIPC #IntelAI #QualcommAI #SnapdragonXElite #LunarLake #PrivateAI #EnterpriseAI #AgenticAI #RAG #VisionAI