Fluidstack is an AI cloud platform that provides NVIDIA GPU-based computing power for AI training and inference scenarios. According to its official introduction, the company claims it "builds compute at a speed no one else on Earth can match," and is committed to building gigawatt-scale data centers in the United States, delivering them within months.
Purpose
Fluidstack's core purpose is to provide the computing infrastructure required for large-scale AI training and inference. The official text states: "AI is trained and run on massive compute clusters," and Fluidstack is precisely the company that builds and operates such compute clusters.
Main capabilities
- Build and operate large-scale GPU compute clusters for AI training and inference.
- Build gigawatt-scale data centers in the United States; the company states it can build a 250MW data center within six months of breaking ground.
- Operate tens of millions of XPUs (various types of accelerated processors) while maintaining high reliability.
- Design compute and power infrastructure, including the deployment of hundreds of gigawatts of solar power.
- Promote onshore manufacturing of all compute and power infrastructure.
- Use closed-loop cooling systems: the cooling water tank is filled once and the water then circulates indefinitely, with replenishment only during maintenance and repairs.
- Invest millions of dollars in each construction community, with the community setting priorities and the company providing funding.
- Develop off-grid solar power to provide electricity for compute expansion.
How to use
No specific registration, purchase, or access process is provided in the official text. Information on how to get started with the Fluidstack platform, pricing models, API documentation, or console entry points is not currently provided. Users can visit the official website https://fluidstack.io/ for the latest information.
Applicable scenarios
- AI model training: requires large-scale GPU clusters for deep learning model training.
- AI inference: deploying trained models to production environments for inference.
- Large-scale computing needs: organizations that require gigawatt-scale data centers and the operational capacity for tens of millions of XPUs.
Limitations
The official text does not specify the platform's specific limitations, such as available regions, GPU model lists, service level agreements, pricing, or free quotas; this information is not currently provided. In addition, statements in the official text such as "a 250MW data center built within six months" and "deploying hundreds of gigawatts of solar power" are descriptions of the company's goals or challenges, and whether they have been achieved or their specific timelines are not clarified.
