: The multiuser server is typically used for versions of Tekla Structures up to version 2020 . For newer versions, Trimble generally recommends Tekla Model Sharing as a more advanced alternative.
: It uses the TCP/IP protocol, typically communicating through port System Location : The default installation folder is usually C:\Program Files (x86)\Tekla Structures Multi-user Server Google Groups Model Conversion
Latency and control.
: It acts as a lightweight hub that manages traffic between different users' local copies of a model and the master model stored on a server. Networking Requirements : By default, the server communicates through TCP port 1238 . This must be open on your firewall for users to connect. Component Parts : The installation typically includes: TeklaMultiUserServer.exe : The core service file. Configuration Files : Used to define specific server settings or logs. Service Manager
Today, we are taking a technical deep dive into versions . While these are not the latest releases (the current numbering has since evolved), many legacy projects and specific client workflows still rely on these stable versions. Tekla Structures Multi User Server 23 20
It is crucial to use the latest multi-user server version available, even if you are using an older version of Tekla Structures. This ensures better stability and compatibility. Best Practices for Multi-User Projects
The Multi User Server is a lightweight service that manages all interactions between users and the master model. Think of it as the dispatcher that coordinates traffic in a busy city. It runs on a dedicated computer (or a virtual server) in your network and handles the crucial tasks of locking objects being edited, assigning IDs to new model components, and tracking which users are active. : The multiuser server is typically used for
Unlike cloud-based repositories that process file deltas independently over the internet, the local multi-user framework splits operational roles across three components:
If you are still using :
Because the server operates on your internal network, data transfer is incredibly fast compared to cloud syncing, making it ideal for large, complex models.