Nvidia announces its most powerful AI chip as it seeks to become a platform company like Microsoft and Apple

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Nvidia on Monday announced a new generation of artificial intelligence chips and software for running AI models.

The announcement comes as companies and software makers still scramble to get their hands on the current generation of H100s and similar chips.on Monday announced a new generation of artificial intelligence chips and software for running AI models. The announcement, made during Nvidia's developer's conference in San Jose, comes as the chipmaker seeks to solidify its position as the go-to supplier for AI companies.

The company also introduced revenue-generating software called NIM that will make it easier to deploy AI, giving customers another reason to stick with Nvidia chips overNvidia executives say that the company is becoming less of a mercenary chip provider and more of a platform provider, like Microsoft or Apple, on which other companies can build software.

"If you're a developer, you've got an interesting model you want people to adopt, if you put it in a NIM, we'll make sure that it's runnable on all our GPUs, so you reach a lot of people," Das said.Every two years Nvidia updates its GPU architecture, unlocking a big jump in performance. Many of the AI models released over the past year were trained on the company's Hopper architecture — used by chips such as the H100 — which was announced in 2022.

Nvidia said that the system can deploy a 27-trillion-parameter model. That's much larger than even the biggest models, such as GPT-4, which reportedly has 1.7 trillion parameters. Many artificial intelligence researchers believe bigger models with more parameters and data NIM makes it easier to use older Nvidia GPUs for inference, or the process of running AI software, and will allow companies to continue to use the hundreds of millions of Nvidia GPUs they already own. Inference requires less computational power than the initial training of a new AI model. NIM enables companies that want to run their own AI models, instead of buying access to AI results as a service from companies like OpenAI.

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