Nvidia’s 3 Major Decisions to Dominate AI

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NVIDIA tackles the challenge of limited high-performance memory on chips by networking multiple chips and servers together. The memory requirements for training AI models are massive and cannot be increased due to technological constraints. NVIDIA's approach involves running huge amounts of memory parallel to processors, ensuring fast data transfer. This unique strategy sets NVIDIA apart in the generative AI and computing era.

It’s a(nother) new era for Nvidia.

We thought we’d closed the Acquired book on Nvidia back in April 2022. The story was all wrapped up: Jensen & crew had set out on an amazing journey to accelerate the world’s computing workloads. Along the way they’d discovered a wondrous opportunity (machine learning powered social media feed recommendations). They forged incredible Power in the CUDA platform, and used it to triumph over seemingly insurmountable adversity — the stock market penalty-box.

But, it turned out that was only the precursor to an even wilder journey. Over the past 18 months Nvidia has weathered one of the steepest stock crashes in history ($500B+ market cap wiped away peak-to-trough!). And, it has of course also experienced an even more fantastical rise — becoming the platform that’s powering the emergence of perhaps a new form of intelligence itself… and in the process becoming a trillion-dollar company.

Today we tell another chapter in the amazing Nvidia saga: the dawn of the AI era. Tune in!

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‍Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.