August 23, 2023
Chipmaker and burgeoning AI platform provider Nvidia posted impressive quarterly results Wednesday, with an astronomical 101% increase in Nvidia revenue from a year ago.
The Santa Clara, California-based vendor announced earnings for the second quarter of its fiscal year 2024 (ending July 30), revealing that it scored a personal best of more than $13.5 billion in revenue. That’s an 88% increase from the previous quarter. The company reported an 854% growth in GAAP earnings per diluted share ($2.48).
Investors had been eagerly awaiting the details on Nvidia’s quarterly revenue, viewing it as a bellwether for the larger generative artificial intelligence (AI) industry. The company has enhanced its portfolio with servers, GPUs and chips that it says enable complex AI workloads. Nvidia has partnered with technology companies like ServiceNow, VMware, Snowflake to power and process AI capabilities.
Nvidia Revenue Report Ushers in ‘New Computing Era’
Nvidia’s Jensen Huang
“A new computing era has begun. Companies worldwide are transitioning from general-purpose to accelerated computing and generative AI,” Nvidia founder and CEO Jensen Huang said in a news release. “Nvidia GPUs connected by our Mellanox networking and switch technologies and running our CUDA AI software stack make up the computing infrastructure of generative AI. During the quarter, major cloud service providers announced massive Nvidia H100 AI infrastructures. Leading enterprise IT system and software providers announced partnerships to bring Nvidia AI to every industry. The race is on to adopt generative AI,” he said.
Nvidia’s data center business in particularly performed well. The practice drove more than $10.3 billion in the latest quarter, a 171% growth from a year prior. That segment features Nvidia’s server GPUs, its DGX cluster servers and its data center networking business, according to Omdia’s Alexander Harrowell.
“These are all central to AI as the networking tech is what links the machines to make the cluster, although there are other uses for the server GPUs like cloud game serving or some supercomputer workloads,” said Harrowell, Omdia’s principal analyst of advanced computing for AI (Omdia is a sister company of Channel Futures).
Harrowell added that has established it as “the industry standard software environment” for AI hardware, creating “a lock” on AI developers. But the latest earnings show that Nvidia must make up ground on supplying all the demand, he said.
Omdia’s Alexander Harrowell
“They can sell all the GPUs they can make, and the limit is the availability, due to the most limiting manufacturing process, TSMC’s CoWoS 3-D packaging line. The 70% gross margin figure implies that their pricing is drifting up and/or that the product mix is getting richer – I think the latter,” Harrowell said. “They can’t go all the way and price until the waitlist disappears as they need to manage customers such as AWS, Microsoft, OpenAI, and a hatful of national labs, as well as keeping some stock for startup/developer access and looking after channel partners. Nor can they really risk underutilizing their allocation of capacity at TSMC after they prevailed on that company to give them substantially more and do super-hot runs, i.e. expedited batches of wafers.”
In the meantime, Nvidia’s automotive division increased 15% from a year ago, and its gaming division went up 22%. However, Nvidia’s professional visualization practice dropped …
… 24% in revenue from a year prior.
“I noticed that automotive wasn’t fantastic sequentially, but gaming and visualization both had a great quarter, which might be interesting in terms of PC demand, which everyone else expects to be grim,” Harrowell said.
Channel Futures asked Bradley Shimmin, Omdia‘s chief analyst of AI and data analytics, if Nvidia really does function as a bellwether for AI. He offered a nuanced response.
Omdia’s Bradley Shimmin
“… Nvidia does act, always a crucial bellwether in predicting future investments in the underlying infrastructure necessary to support AI workloads. That said Nvidia doesn’t drive AI innovation in whole,” Shimmin told Channel Futures. “The ecosystem that focuses on AI hardware acceleration is surprisingly rich, despite Nvidia’s dominant market position. Hyperscaler, providers, Google, and AWS, as well as hardware manufacturers AMD, Intel and many others are also moving the market forward in terms of delivering infrastructure tuned to the specific demands of AI.”
Alpine’s Bill Blum
Bill Blum is the founder and President of the MSP and software development company Alpine Business Systems. He said he has been regulatory meets with vendors who want to demonstrate their AI products, and he said he’s interested in playing different vendor ecosystems if he finds a good portfolio fit.
But in the meantime, Blum said he has found Microsoft’s current AI stack compelling. He has brought up Microsoft Copilot’s various features on calls with customers, with the goal of drawing attention to capabilities that already exist.
“Generative AI is one thing. It’s the engine behind it, that Microsoft is embedding all of their products in that I find very interesting and am able to bring to my customers. They’re beginning to use analysis on speech and Teams meetings. Obviously the translation is incredible. It’s all tools that every single business can use if they knew about it. So our mission has been to help them understand how they can use these very simple tools that are embedded in the products they already own,” Blum told Channel Futures.
Moreover, Blum said some members of Alpine’s customer base – mainly small and medium-sized businesses – have reached out about using AI to mine existing data. They include financial services providers and manufacturers. Alpine is entering the early phase of identifying a plan for serving that request.
“We’re in the top half of the first inning here. We see what’s out there. We know what the game sort of is, and we’re just working our way through the steps that it will take for all of these small and medium businesses that we work with to begin to put this technology on the datasets that they actually have,” he said.
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