Panel AI

Panel AI supports x86 and ARM architectures, can be deployed on public cloud platforms, and also supports a variety of AI chips.

Panel AI supports selling surplus computing power externally, and applications can be listed for sale directly in the backend.

Panel AI supports enterprises in scaling scheduling resources by simply buying more servers and connecting them to the system, and the built-in scheduling system automatically determines resource needs based on project usage.

Panel AI supports freely switching and combining open-source models, and enterprises can choose different models according to business scenarios.

Projects deployed with Panel AI run their code in containers, and enterprises can modify it directly, with all documentation provided.

The above information about Panel AI mainly comes from Xiong Ge's one-sided sharing; the video did not show specific performance test data, details of the multi-tenant isolation scheme, or stability performance under large-scale nodes.

Panel AI achieves deployment from zero to production in under 30 minutes.

Panel AI supports horizontal scaling — users simply click to add a new server in the interface, and the new node automatically joins the cluster and performs load balancing, with no perceived interruption to users and no service downtime.

Panel AI is positioned as an AI infrastructure platform whose core value is lowering the barrier to private deployment.

Panel AI fully automates port configuration, firewalls, and security groups.

The entire process of deploying Dify through Panel AI takes approximately 5 minutes and can be completed even with zero prior experience.

Panel AI supports multi-account isolation, where each user's created containers and data are independent, so their knowledge bases and conversations do not conflict with one another.

The UP host advises ordinary teams not to tackle private AI deployment from scratch locally, and recommends using mature platforms for efficiency-focused scenarios.

The article notes that Panel AI's automation capabilities and scaling performance are based solely on a single UP host's demonstration and opinions, and still need to be verified through real-world testing according to one's own business scale.

Because the Laya image contains the model and is relatively large, using free image acceleration services directly is prone to jitter that causes installation failures, so it is recommended to first configure the server's network environment before installing.

After deploying Laya on Panel AI, the page provides an API address that can be pasted into any application supporting that API request address to connect to Laya and complete decision tasks.

Panel AI supports multi-machine cluster mode, allowing continuous addition of nodes to manage multiple machines, with authorization completed through binding and activation, and permanent licensing with no expiration date.

Panel AI supports both vLLM and Ollama runtime frameworks, and users can choose according to their needs.

The private deployment process for Panel AI is: copy the installation command provided by the system and execute it on the server, after which the frontend and backend addresses along with the account credentials are automatically generated and the ports that need to be opened are prompted.

Currently, some hardware vendors are in talks for cooperation, hoping to pre-install Panel AI on servers at the factory.