Innovation in Chip Design
Sarah highlights the challenges of training large-scale models without extensive testing on specific chips, emphasizing that true validation requires real-world workloads. She points out that while Nvidia has established itself through years of optimization, new contenders face significant barriers due to the high costs and risks associated with large-scale deployment. The conversation also touches on the broader implications of technological innovation and the potential for a bubble in the current investment landscape.In this clip
From this podcast
Invest Like the Best
Sarah Guo - The Power of Conviction - [Invest Like the Best, EP.383]
Related Questions
Can competitors catch up in GPU cloud services as discussed in the episode Nvidia Part II: The Machine Learning Company (2006-2022) and the clip Giant Chips, Hyper Specialized Hardware
Can competitors catch up in GPU cloud services as discussed in the episode Andrew Homan & Chris Miller - Redefining Semiconductor Progress - \[Invest Like the Best, EP.395] and the clip Innovation Bottlenecks?
Can competitors catch up in GPU cloud services as discussed in the episode Inside the Battle for Chips That Will Power Artificial Intelligence and the clip Custom Chips Revolution?