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When Jensen Huang said AIM that the world we live in is built in NVIDIA GPU, I wasn’t exaggerating. The company controls about 80% of the accelerator market in AI data centers operated by AWS, Google Cloud and Microsoft Azure.
Recently, he played a $2 trillion market value and added $277 billion in stock market value on Thursday – Wall Street’s biggest one-day gain in history.
The company reported revenue of $22.1 billion, an increase of 22% sequentially and a remarkable 265% year-on-year—well above the $20 billion outlook—during the last earnings call. “The world has reached the tipping point of a new computing era,” he said. Colette KressNVIDIA CFO, during the earnings conference call.
“Almost every time you interact with ChatGPT, we make inferences. Every time you use Midjourney, we are inferring. Every time you see amazing videos of Sora being generated or Runway, the videos they are editing, Firefly, NVIDIA is making inferences.” saying Huang, on the recent earnings conference call. He said that his AI supercomputers are essentially AI generation factories of this industrial revolution.
NVIDIA has NO brakes
Since Huang personally delivered the first DGX-1 AI supercomputer to OpenAI in 2016, NVIDIA has come a long way. Today, generative AI startups like Anthropic, Inflection, and xAI are among the examples that rely heavily on NVIDIA GPUs, specifically RTX5000 and H100to keep your generative AI services running.
Earlier this year, Meta boss Mark Zuckerberg revealed that the company is currently training Llama 3 and plans to purchase 350K NVIDIA H100s by the end of this year. Additionally, the social media giant has set its sights on developing open source AGI.
NVIDIA reportedly invested in more than 30 AI startups: “It is a privilege for us to invest in them, and not the other way around. “These are some of the greatest minds in the world.” saying Huang.
“Interesting companies like Adept, AI21, Character.ai, Cohere, Mistral, Perplexity and Runway are creating platforms to serve businesses and creators,” Kress said, adding that new startups are creating LLMs to serve specific languages, cultures and customs. . from many regions of the world.
NVIDIA is making significant investments in healthcare and drug discovery. Recursion Pharmaceuticals, in which NVIDIA invested $50 million, now offers its proprietary AI model through BioNeMo to the drug discovery ecosystem.
In the enterprise segment, NVIDIA is also working with “leading enterprise software and artificial intelligence platforms, including Adobe, Databricks, Getty Images, SAP and Snowflake,” Kress said.
indian eyes
Last year, NVIDIA promised that India would receive tens of thousands of GPUs and partnered with Dependence and Tata, along with government collaboration, which plans to establish a pool of 25,000 GPUs for startups.
“India will be one of the first countries in the world (to get them),” Huang said, confirming that they would be faster than anything the world has ever seen.
NVIDIA is also helping to upskill talent in the country. The company partnered with Infosys to train 50,000 people in generative AI. Also partnered with TCS to enhance skills more than 6 lakh employees in generative AI. Meanwhile, Partner Wipro with NVIDIA to help healthcare companies accelerate adoption of generative AI.
“India has a lot of data,” Jensen said, referring to the diversity of languages and dialects. He said: “There is no reason for India to export data to Western companies.
However, last year, he mentioned that India lacks infrastructure: “not the kind of roads and bridges,” but artificial intelligence infrastructure. He said that with the advent of NVIDIA supercomputers, that has also been solved.
India is now looking to challenge global hyperscalers as well. Yotta, the Mumbai-based data center giant, plans to deploy 32,000 NVIDIA H100 and H200 GPUs by 2025 worth around $1 billion. Approximately 16,384 GPUs by mid-2024 and 32,768 GPUs by the end of 2025.
Older players such as NSE-listed E2E Networks have also been expanding their presence in the country, with adani also rapidly expanding. Indian SaaS company Zoho is also using NVIDIA GPUs to create its own LLM to add GenAI capabilities into its suite in a bid to reduce dependence on hyperscalers.
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Whats Next?
Last year, NVIDIA faced challenges in GPU demand, but this year it has improved the supply chain, according to Huang. “Our supply is improving overall,” he said, adding that their supply chain is doing an incredible job for them, from wafers and packaging to memories, power regulators, transceivers, networks and cables. There is still a shortage, but NVIDIA has increased production of the H200.
NVIDIA plans to launch black, a new range of GPUs that promises improved AI computing performance compared to the current Hopper architecture, potentially reducing the need for multiple GPUs
Additionally, the company plans to build the next generation of modern data centers, what it calls AI factories, designed specifically to refine raw data and produce valuable intelligence. “In the future, every car company will have a factory that builds the cars (the real goods, the atoms) and a factory that builds the AI for the cars, the electrons,” Huang said.
Huang also aims to build a sovereign AI infrastructure around the world. “What is being experienced here in the United States, in the West, will surely be replicated around the world, and these AI generation factories will be in every industry, every company, every region,” Huang said.
NVIDIA is ready to host its flagship GTC conference at the San José Convention Center from March 18 to 21, 2024. More than 300,000 people are expected to attend this event (both in person and virtually). “I’m going to tell everyone about a lot of new things we’ve been doing. working on the next generation of AI” saying Huang.