Hardware requirements vary for machine learning and other compute-intensive workloads. Get to know these GPU specs and Nvidia GPU models. Chip manufacturers are producing a steady stream of new GPUs.

What if the key to unlocking faster, more efficient machine learning workflows lies not in your algorithms but in the hardware powering them? In the world of GPUs, where raw computational power meets ...

TPUv7 offers a viable alternative to the GPU-centric AI stack has already arrived — one with real implications for the economics and architecture of frontier-scale training.

Samsung is preparing a major shift with the Exynos 2800, set to debut in 2027 as the company’s first chipset featuring a ...

Nvidia announced that it’s acquiring Run:ai, an Israeli startup that built a Kubernetes-based GPU orchestrator. While the price is not disclosed, there are reports that it is valued anywhere between ...

What if you could get professional-grade GPU performance without breaking the bank? Intel’s latest release, the Pro B50 GPU, is turning heads, and not just for its specs. At a jaw-dropping price of ...

Conrad Sanderson does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

Neural architecture search (NAS) and machine learning optimisation represent rapidly advancing fields that are reshaping the way modern systems are designed and deployed. By automating the process of ...