Transcript: It's interesting that NVIDIA's trend throughout its history has been making very excellent software for its hardware, as in initially bringing drivers in house, according to Asianometry. And how, I mean, if we look forward to today, how them being able to focus on software for their hardware has enabled them to be as big of a company as they are, and to be a force in the AI industry is only because of their drivers. At the end of the day, it's because of CUDA. So it's interesting to see how important software is for hardware. And yeah, I mean, it just makes me think maybe I need to look at llama.cpp and all of these open source libraries to accelerate hardware, as well as maybe there's some frameworks that still need help. And what ways can we accelerate them? Because I don't mind being on the boundary of software and hardware. I worked on that boundary already, effectively firmware, or right above firmware, and I can be comfortable in that place. And managing memory and all of that is, it's an interesting thing. And to work on something massively parallel, like a machine learning inference card, would be fascinating and also quite important, I think. So that's a fun little tangent, I guess, of possibilities.
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The cost of computing power is expected to decrease, leading to increased availability. This makes the ability to utilize this computing power for extensive processing or post-processing very important, especially with evolving hardware architectures. If supported, doing massively parallel inference and leveraging large language models for parallel post-processing will likely be both feasible and significant. The trend towards more accessible compute resources will thus play a pivotal role in the advancement of post-processing capabilities and the application of large language models.
The speaker reflects on the customer-focused approach of TSMC and its importance in business, while also discussing a personal struggle with balancing self-focus and advancing the interests of others. They find satisfaction in moving the ball forward for other people and are working to create tools that fulfill initial promises and improve ease of use. The "burrito project" mentioned seems to involve creating fundamental tools, exploring AI, and making computer tools more accessible, highlighting the magic and potential of fetching personal data with simple requests.
The author is considering the dilemma between renting and buying AI hardware, particularly GPUs, for a company that requires significant compute resources to take off. Renting encourages minimal use of funds, which conflicts with the need for extensive GPU utilization to create something noteworthy. The author suggests that constantly running GPUs at full capacity for inference is a unique strategy that could provide a competitive edge by allowing real-time, high-performance applications. This approach implies a constant inference process on data, making it more accessible and valuable for sorting and classifying, a concept the author is pondering on.
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The speaker emphasizes their unique strategy regarding AI, recognizing the existing interest in such pervasive technology and its demonstrated potential. They argue that success in this field isn't solely about attractive designs but also about hiring the best engineering talent to make technological advancements possible. Acknowledging their own limitations, the speaker notes the importance of fundamental technology developed by their friends and the need for substantial technological work, implying that simple technology orchestration is not enough for sustained success. Despite the rambling nature of their thoughts, the speaker seems to aim for a blend of business and consumer offerings, driven by core technological innovation and top engineering expertise.
The speaker is reflecting on the use of local large language models and the potential impact on the technology industry. They contemplate the reasons behind using local AI and express a desire to delve deeper into the topic. Additionally, they explore thoughts about their future aspirations of potentially becoming a venture capitalist and their excitement for shaping potential futures. The speaker also ponders about whether large language models will be implemented locally on devices and considers the potential influence of companies like Apple on the hardware market. They discuss the uncertainty around upcoming software development kits and the need to prepare for that transition. The speaker concludes with a remark about the thick fog outside and indicates a temporary pause to focus on driving.