Pipeshift has a Lego-like system that allows teams to configure the right inference stack for their AI workloads, without extensive engineering.

DLSS 4 is arguably the biggest selling point of the new RTX 50-series, but any Nvidia RTX GPU can benefit. Here's how.

The explosive growth of ChatGPT has triggered unprecedented demand for artificial intelligence (AI) computing power, leading to industry-wide supply constraints. While Nvidia maintains its stronghold as the premier AI GPU provider,

NVIDIA's next-gen Rubin AI GPU architecture expected to enter 'trial production' in 2H 2025, SK hynix is moving faster than ever to get HBM4 ready.

The Biden export limits, enacted during his last days in office, may drive data center construction to US allies while forcing China and Russia to develop their own AI chips.

Qdrant, the developer of a high-performance open-source vector database, today introduced its graphics processing unit accelerated vector indexing capability that will make scaling up artificial intelligence applications easier.

This chip cojoins existing AMD CPU and GPU architectures, along with a high speed memory interface, in an unprecedented way, compared to traditional X86 chip designs.

The technical evaluation of the bids was done on January 13, sources said, adding that New Delhi-based E2E Networks and Bengaluru-based NxtGen Datacenter and Cloud Technologies are among the shortlisted firms.

Nvidia has purportedly disabled overclocking and multi-GPU support on the RTX 5090D to ensure its performance does not exceed U.S. export regulations.

So, how can Nvidia's stock soar 67% in 2025? Simple. It does what it's expected to and gives a solid outlook for next year. Right now, Nvidia trades for 52 times trailing earnings, which is near the cheapest level it has traded at over the past two years.

The growing demand for advanced AI has led to a massive surge in computing power needs, prompting the rise of GPU-as-a-Service (GPUaaS) businesses. According to IEEE Spectrum, as artificial ...

Qdrant’s hardware-agnostic approach to GPU acceleration enables speed index-building with support for most modern GPUs to give users the flexibility to efficiently process massive datasets while adopting and using the most suitable infrastructure for their real-time AI applications based on technical, cost and other considerations.