When Jensen Huang told AIM that the world we live in is built on NVIDIA GPUs, he wasn’t exaggerating. The company controls about 80% of the market for accelerators in the AI data centres operated by AWS, Google Cloud and Microsoft Azure.

Recently, it touched a $2 trillion market value and added $277 billion in stock market value on Thursday—Wall Street’s largest one-day gain in history. 

The company reported a revenue of $22.1 billion, up 22% sequentially and a remarkable 265% year-on-year—well above the outlook of $20 billion—during the latest earnings call. “The world has reached the tipping point of a new computing era,” said Colette Kress, chief financial officer of NVIDIA, during the earnings call.

“Almost every single time you interact with ChatGPT, we’re inferencing. Every time you use Midjourney, we’re inferencing. Every time you see amazing Sora videos that are being generated or Runway, the videos that they’re editing, Firefly, NVIDIA is doing inferencing,” said Huang, at the recent earnings call. He said that its AI supercomputers are essentially AI generation factories of this industrial revolution. 

There is NO Stopping for NVIDIA 

From Huang personally delivering 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 heavily rely on NVIDIA GPUs, specifically RTX 5000 and H100s, to keep their generative AI services running. 

Earlier this year, Meta chief 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’s a privilege for us to be investing in them, not the other way around. These are some of the brightest minds in the world,” said Huang. 

“Exciting companies like Adept, AI21, Character.ai, Cohere, Mistral, Perplexity, and Runway are building platforms to serve enterprises and creators,” said Kress, adding emerging new startups are creating LLMs to serve the specific languages, cultures, and customs of many regions worldwide. 

NVIDIA is making significant investments in healthcare and drug discovery. Recursion Pharmaceuticals in which NVIDIA invested $50 million is now offering its proprietary AI model through BioNeMo for the drug discovery ecosystem.

In the enterprise segment, NVIDIA is working with “leading AI and enterprise software platforms as well, including Adobe, Databricks, Getty Images, SAP and Snowflake”, said Kress.

Eyes India 

Last year, NVIDIA promised India would receive tens of thousands of GPUs and partnered with Reliance and Tata, alongside the government collaboration, which plans to establish a cluster of 25,000 GPUs for startups. 

“India will be one of the first countries in the world [to get them],” Huang said, confirming that these would be faster than anything the world has ever seen. 

NVIDIA is also helping in upskilling the talent in the country. The company partnered with Infosys to train 50K on generative AI. It also partnered with TCS to upskill over 6 lakh employees in generative AI. Meanwhile, Wipro partnered with NVIDIA to help healthcare companies accelerate the adoption of generative AI. 

“India has lots of data,” said Jensen, touching upon the diversity of languages and dialects. He said, “There’s no reason for India to export data to Western companies. 

However, last year, he mentioned that India does lack infrastructure – “not the roads and bridges kind”, but AI infrastructure. He said with NVIDIA supercomputers coming in, that has also been taken care of. 

India is now looking to challenge global hyperscalers as well. Mumbai-based data centre giant Yotta plans to deploy 32,000 NVIDIA H100 and H200 GPUs by 2025 worth about $1 billion. Roughly 16,384 GPUs by mid-2024 and 32,768 GPUs by the end of 2025.

Older players, like the NSE-listed E2E Networks, have also been extending their footprint in the country, with Adani also expanding rapidly. Indian SaaS company Zoho is also utilising NVIDIA GPUs to build its own LLM to add GenAI capabilities across its suite in an attempt to reduce the reliance on hyperscalers. 

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What’s Next? 

Last year, NVIDIA faced GPU demand challenges, 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 just doing an incredible job for them from wafers and packaging to memories, power regulators, transceivers, networking, and cables. There is still a shortage, but NVIDIA has ramped up H200 production.

NVIDIA plans to launch Blackwell, a new GPU range that promises improved AI compute 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 centres, what it refers to as AI factories, purpose-built to refine raw data and produce valuable intelligence. “Every car company in the future will have a factory that builds the cars—the actual goods, the atoms—and a factory that builds the AI for the cars, the electrons,” said Huang.

Huang is further targeting to build sovereign AI infrastructure worldwide. “What is being experienced here in the United States, in the West, will surely be replicated around the world, and these AI generation factories are going to be in every industry, every company, every region,” said Huang.

NVIDIA is set to host its flagship GTC conference at the San Jose Convention Center from March 18-21, 2024. Over 300,000 people are expected to attend this event (both in-person as well virtually). “I am going to tell everybody about a whole bunch of new things we’ve been working on the next generation of AI,” said Huang.