Last quarter, HPE took its lumps over supply chain issues that hampered its AI server sales. In the second quarter, AI systems revenue more than doubled sequentially.
AMD outlined its AI GPU roadmap and moved to an annual cadence as it aims to compete with Nvidia.
Nvidia CEO Jensen Huang outlined the company's roadmap through 2027 including a new GPU platform called Rubin, a new CPU in Vera and networking gear. Huang added that Nvidia will follow an annual cadence.
Dell Technology reported better-than-expected first quarter results and said it saw strong demand across traditional and AI optimized servers.
AMD CFO Jean Hu said cloud providers and enterprises are starting to look toward total cost of ownership when it comes to inference and training workloads for artificial intelligence workloads.
Nutanix outlined a series of deployment options, partnerships and platform enhancements at its .Next Conference in Barcelona. The gist of the news: VMware we have you surrounded.
Dell Technologies is going all-in on AI factories and enabling generative AI workloads powered by a wide range of third parties as well as Nvidia-flavored efforts.
In the last 12 months, CoreWeave has raised more than $12 billion in equity and debt financing. CoreWeave's funding highlights how the generative AI boom has fueled a new category of specialized cloud vendors.
Arm Holdings' chip designs may take over the data center over time as GPUs, cloud custom processors and Nvidia's march to AI factories gains momentum. But the road to licensing and royalty nirvana is going to be lumpy.
By unit, AMD posted record data center revenue in the first quarter of $2.3 billion, up 80% from a year ago. Growth was driven by AMD Instinct GPUs and 4th Gen AMD EPYC CPUs.
The chipmaker, which is trying to catch up in AI processors, said it expects second quarter revenue between $12.5 billion to $13.5 billion, well below the $13.61 billion Wall Street expected.
The second version of the Meta Training and Inference Accelerator (MTIA) highlights how cloud hyperscale players are creating their own processors for large language model (LLM) training and inferencing.
