Nvidia reported a better-than-expected third quarter, raised its outlook and said that Blackwell shipments will begin in the fourth quarter. Data center revenue in the third quarter was up 112% from a year ago.
Nvidia said that it is working with Google Quantum AI to design quantum computing AI processors. The quantum computing partnership with Google was part of a series of Nvidia announcements at the SC24 conference in Atlanta.
Softbank Corp will be among the first to build out an AI supercomputer using Nvidia's Blackwell platform. Softbank will get Nvidia's first Nvidia DGX B200 systems with plans to build out a Nvidia DGX SuperPOD supercomputer.
Nvidia has had a steady stream of NIM Agent Blueprint news as it aims to make agentic AI more commonplace in enterprises.
Accenture has formed a Nvidia Business Group that will deploy agentic AI using Nvidia's full stack. The move puts some systems integrator heft behind Nvidia's software ecosystem.
NVLM 1.0 will enable smaller enterprises and researchers to piggyback off Nvidia's research.
Nvidia said its Blackwell processor samples are shipping to customers and demand for its Hopper platform remains strong. The company reported better-than-expected second quarter earnings as data center revenue continued to carry the team.
The general availability of H200-powered systems comes as Nvidia released MLPerf Inference v4.1 benchmarks for its Hopper architecture and its first Blackwell platform submission.
Cerebras Systems, a startup focused on building AI systems and processors, launched Cerebras Inference, which the company claims is 20 times faster than Nvidia GPU-based instances in hyperscale clouds.
Nvidia launched NIM Agent Blueprints, which are reference AI workflows aimed at bringing generative AI to specific use cases.
Nvidia has developed a system that preserved large language model (LLM) accuracy with less precision. The system, which will be outlined at the Hot Chips conference, highlights how Nvidia's AI game is becoming more about software and optimization as much as it is hardware.
The spending on generative AI infrastructure is accelerating at a breakneck pace, but it's quite possible that the "build it and they will come" approach may lead to overcapacity or some serious indigestion.
