12/08/2026
Cloud or local? AI is becoming a hardware decision again.
For many businesses, using AI has meant relying almost entirely on cloud infrastructure.
That is starting to change.
As organisations move from experimenting with AI to developing internal applications, running certain AI workloads locally is becoming a practical option.
Local AI infrastructure can offer several advantages:
• Greater control over data, particularly when working with sensitive or proprietary information
• More predictable compute costs for workloads that are used or tested frequently
• Faster development cycles, without depending on remote infrastructure for every experiment
• A controlled environment for prototyping, fine-tuning and running AI models
Cloud infrastructure still has an important role, particularly where flexibility and large-scale compute are required. But increasingly, the question is not simply cloud or local. It is which workloads belong where.
And that means hardware is becoming part of the AI strategy.
The Lenovo ThinkStation PGX, powered by the GB10 Grace Blackwell Superchip, is designed specifically for this type of local AI development. It provides a desktop environment for prototyping, fine-tuning and inference, with support for AI models of up to 200 billion parameters.
For businesses exploring private AI, local LLMs or hybrid AI infrastructure, the workstation on your desk may now have a much bigger role to play.
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Talk to our team to explore whether local AI infrastructure is right for your business.
