
Nvidia to Invest $2.1 Billion in IREN to Scale AI Data Center Capacity
Nvidia announces a $2.1 billion investment in IREN to deploy 5GW of AI data center capacity, reinforcing the…
Latest news on GPU cloud computing, AI infrastructure, and data center developments.

Nvidia announces a $2.1 billion investment in IREN to deploy 5GW of AI data center capacity, reinforcing the…

US prosecutors suspect Super Micro servers with advanced Nvidia AI chips were allegedly smuggled to Alibaba via Thailand,…

A new F5 report indicates a growing trend of enterprises bringing AI inference workloads in-house, driven by security,…

Anthropic secures access to xAI’s Colossus supercomputer to train future Claude models, marking a major shift in the…

AI data centers face energy volatility that lithium-ion batteries can't handle. Explore why the industry needs new buffering…

Explore how minimalist floating structures and thermal management trends are influencing the next generation of liquid-cooled AI data…

Nvidia and Corning have announced a partnership to ramp up production of key AI components, addressing supply chain…

AI workloads are pushing rack densities to 100kW, forcing data center operators to rethink power distribution and grid…

Super Micro projects robust Q4 revenue, betting on surging AI server demand despite a Q3 revenue miss. Learn…

AMD exceeds Q2 revenue expectations, driven by robust demand for its data center AI chips. Learn about the…

Micron launches the 245TB 6600 ION SSD, targeting AI data lakes and hyperscale infrastructure to improve storage density…

Span, in collaboration with Nvidia and PulteGroup, is deploying mini data centers on homes, leveraging liquid-cooled RTX GPUs…
The AI infrastructure market changes rapidly. Hardware releases, such as the NVIDIA H200 and AMD MI300X, dramatically shift the pricing landscape for legacy chips like the A100 and V100.
Our insights deliver data-driven analysis on GPU pricing trends, cloud provider expansion news, and benchmarking guides. Stay ahead of the curve to optimize your machine learning pipeline costs and ensure you're deploying on the most performant architecture available.