On-Premises AI Deployment

Why GPU Memory is the Real Bottleneck in AI Infrastructure

The conversation around AI infrastructure focuses on FLOPS and GPU count, but in practice memory is what determines what models you can run. A 70B parameter model needs at least 140GB of GPU memory in FP16, far exceeding what a single GPU offers — and this constraint shapes nearly every infrastructure decision.

Americans Oppose AI Data Centers in Their Backyards

A recent Gallup survey reveals that 70% of Americans oppose having AI data centers near their homes, showing a strong preference against localized tech infrastructures, which is a greater disapproval rate than that of nuclear power plants. This sentiment highlights growing concerns about privacy and the potential impact of such facilities on neighborhoods.

What is AI Infrastructure?

AI infrastructure is the hardware, software, and networking layer that lets AI models train and run at scale. It includes GPU clusters, specialized chips, distributed storage, and the orchestration systems that coordinate them. Without solid infrastructure, even the best AI models can't reach real users.

Satya Nadella warns companies relying on a single AI may not survive

In a recent statement, Satya Nadella highlighted the risks for companies that depend solely on one AI system. He argues that firms without their own models or an AI infrastructure known as AI gateways might find themselves at a significant disadvantage in an increasingly competitive market.

SoftBank is creating a robotics company that builds data centers and eyes a $100B IPO

SoftBank is venturing into the robotics industry by developing a company that focuses on building data centers. This ambitious move indicates a fusion of advanced technology and infrastructure, with plans for a monumental $100 billion IPO, showcasing the growing importance of AI in enterprise solutions.