The team received the Test of Time Award for their paper, GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server. The paper addresses the challenges of scaling deep ...

Enterprise AI workloads require infrastructure designed for large-scale data processing and distributed computing. Organizations are modernizing AI data center infrastructure with GPU computing, ...

CtrlS says AI is forcing data centres to rethink power, cooling, sovereignty, and compute access as enterprises move toward ...

Explore Nebius, the AI cloud built for GPU intensive training, scalable inference, managed ML tools and real world AI workloads.

When data movement is delayed, even the fastest compute engines are left waiting, reducing throughput, increasing latency, ...

The deal will significantly expand US optical fibre manufacturing capacity, strengthen domestic supply chains and accelerate ...

Sales of Intel's central processing units and custom AI processors are gaining traction as AI inference workloads grow.

The government has unveiled a dual support package for the leather industry ahead of Eid-ul-Azha, deciding to distribute ...

Here's how distributed compute, latency, cost and resilience are reshaping infrastructure strategy for business leaders.

Frontier LLMs are evolving away from the dense and homogeneous AI workloads that originally favored GPU architectures.

In-chip monitoring restores trust through predictive maintenance that can identify and correct errors in real time.