Configure Lenovo ThinkStation PGX for business
Timo Wevelsiep•Updated: 15.08.2026Editorial note: Versions, commands and prices may change. Please verify critical steps independently before production use. This guide does not replace individual consulting.
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Lenovo ThinkStation PGX is Lenovo's compact AI workstation based on NVIDIA's GB10 Grace Blackwell Superchip. It offers 128 GB of unified memory, DGX OS, and ConnectX-7. Lenovo primarily targets AI development and local model work. An internal platform for employees additionally requires a usable interface, controlled access, and reliable operations.
Technical context
Lenovo documents up to 4 TB of self-encrypting NVMe storage, 10 GbE, Wi-Fi 7, and a 200 Gbps ConnectX-7 link. A 20-core Arm processor and Blackwell GPU share 128 GB of coherent memory.
PGX is not a conventional x86 Windows workstation. Containers and dependencies must support ARM64, so existing internal software should be checked before migration.
Select a model, not a specification limit
Lenovo states models up to 200 billion parameters on one system and 405 billion across two connected PGX units. This does not describe chat latency or concurrent user capacity.
Selection depends on model architecture, quantisation, task quality, context length, concurrent requests, target response speed, and headroom for KV cache and platform services.
Configure it as a business platform
Production setup covers system updates, named administrators, network and firewall rules, local model runtime, Open WebUI, persistent data, backup, and monitoring. Model access and knowledge spaces are separated by role.
Employees then work through a browser rather than developer tooling. They can use approved models and maintain personal or shared knowledge. Automated Nextcloud, SharePoint, or DMS sources are implemented as a separate integration scope.
When ThinkStation PGX fits
PGX is a good fit where Lenovo already supplies workstations and procurement wants to place a compact Linux AI platform in that vendor context. Technically it is close to other GB10 systems; practical differences mainly concern SSD configuration, availability, warranty, and supply chain.
The decision should define whether the device is a developer workstation or a central service. For the latter, fixed network paths, independent backup, and clear operational ownership matter more than local display connections. Existing x86 applications should be checked for ARM64 availability before deployment.
WZ-IT AI Cube Pro and clustering
The AI Cube Pro combines hardware, Open WebUI, runtime, an agreed model, hardening, functional testing, initial setup, and five hours of support. WZ-IT can also provide updates, monitoring, and support.
A second device can serve more independent requests or distribute a larger model. The approaches need different configurations. See connecting AI Cubes with ConnectX-7.
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Frequently Asked Questions
Answers to the most important questions
Lenovo positions PGX for local AI development. GB10 and 128 GB of unified memory also make it a foundation for internal inference and assistants when a user platform and operations are added.
Lenovo states up to 200 billion parameters on one device and 405 billion across two connected systems. Quantisation, context, and required speed determine practical model choice.
No. The GB10 platform uses an Arm processor and NVIDIA DGX OS. Software, containers, and administration must be compatible with this Linux and ARM64 environment.
It includes a prepared AI platform with Open WebUI, runtime, an agreed model, hardening, functional testing, integration, initial setup, and five hours of support.
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