# Deploy Tinkerblox UltraEdge HPC-I for AI and mixed workloads on Arm

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/)
- [Explore UltraEdge HPC-I architecture for edge AI](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/background/)
- [Provision a Google Axion C4A VM for Yocto image builds on Arm](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/instance/)
- [Build Yocto images for NXP S32G-VNP-GLDBOX3 with UltraEdge](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/yocto_installation/)
- [Install UltraEdge on Debian and Ubuntu for Edge AI workloads](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/debian_installation/)
- [Run and manage UltraEdge HPC-I for AI and mixed workloads on Arm](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/running/)
- [Next Steps](https://learn.arm.com/learning-paths/cross-platform/tinkerblox_ultraedge/_next-steps/)

## About this Learning Path

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| Skill level: | Advanced |
| Reading time: | 1 hr |
| Last updated: | 03 Nov 2026 |

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| Author: | Karthik Gopalakrishnan , Tinkerblox |
| Arm IP: | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags: | [Containers and Virtualization](https://learn.arm.com/tag/containers-and-virtualization/) [Google Axion](https://learn.arm.com/tag/google-axion) [Linux](https://learn.arm.com/tag/linux) [other](https://learn.arm.com/tag/other) [Tinkerblox](https://learn.arm.com/tag/tinkerblox) |

### Who is this for?
This is an advanced topic for business, R&D, and engineering teams seeking to optimize CPU and GPU infrastructure utilization while reducing total cost of ownership on edge and constrained environments. It's ideal for innovation and development teams building next-generation AI workloads using alternative runtime environments and packaging technologies.

### What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Understand the layered architecture of UltraEdge core, boost, and prime
- Build applications using the UltraEdge MicroStack
- Deploy the MicroPacs on Linux-based compute systems and scale to cloud or data-center environments
- Optimize performance for edge-cloud scenarios, enabling near real-time data flows

### Prerequisites
Before starting, you'll need the following prerequisites:
- Experience using Linux on embedded or SBC platforms
- Understanding of container runtimes (containerd) and CNI networking
- Basic knowledge of communication protocols (MQTT, HTTP, and others)
- Familiarity with edge-cloud architectures and data-flow orchestration

### Summary
You’ll explore Tinkerblox UltraEdge, an edge-native execution fabric for AI and mixed workloads on Arm platforms. First, you’ll review the UltraEdge architecture, provision a Google Axion C4A virtual machine for an `arm64` Yocto build, and install the UltraEdge agent and MicroPac tooling on Debian or Ubuntu. Then, you’ll build and deploy MicroPac workloads, create a Yocto image for an `NXP S32G-VNP-GLDBOX3` board, and manage services with the Tinkerblox and MicroBoost CLIs. The workflows cover both package-based Linux installations and Yocto-based device deployments.