Transcript: One of the things I'm thinking about this morning is GPUs or AI hardware, generally speaking, and kind of the tension between renting it versus buying it. In the sense, renting it is okay, but the incentive of renting is to use as little of the money as possible. Which is a bad incentive if you're a company trying to get off the ground who needs to leverage a lot of compute. And at this moment, it seems like we all need to leverage a lot of GPU compute to do something interesting. And you can try to make optimizations, but if you can leverage massive amounts of compute with really good training data, and you can keep those GPUs utilized at 100% all the time to be doing inference in the background so your application can run at high performance, that seems to be something no one else is doing or thinking about. And everyone else will just rack up dollars to run inference, basically. Like, even me alone on a body of data, I can be spending $5 or $10 a day at OpenAI because I'm renting their compute. And that's not even me running it 24-7. But really, the ideal thing for me to do is more or less be running inference all of the time. There's many, many things to infer from the data. And setting the data up in a way that has inference done on it already is very, very valuable. Because that allows sifting and sorting of data as well. So, anyway... Yeah, thoughts on inference.
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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 tension between ownership and renting, particularly in the context of technology and cloud computing, is a curious and complex topic that the speaker is exploring. They highlight the idea of effectively renting compute time and space through services like OpenAI and Microsoft Azure, and how reliance on the cloud has become a prevalent aspect of modern computing. The speaker also acknowledges the convenience of storing data in the cloud, even though they may own a computer. They mention OpenAI's work in this area, specifically their assistance API, and express a desire to further explore and challenge their own perspectives on these concepts.
The writer discusses the contrast between ownership and rental, noting that ownership usually represents a capital asset while renting is just an expense. The consideration of owning versus renting becomes relevant when ownership is cost-effective compared to renting. The writer questions the economic implications of owning technology, such as gaming PCs, and how certain technologies may not qualify as capital assets due to depreciation. The discussion then shifts to the potential development of a dedicated chip, such as a GPT-4 ASIC, and the feasibility of widespread adoption and cost-efficiency compared to cloud services. Ultimately, the writer expresses a preference for ownership over renting, as it provides leverage in the world, particularly in relation to valuable tech companies that own the IP and computational resources for large language models. The text reflects on the potential commoditization of computing resources and its impact on the industry. The speaker believes that compute resources will inevitably become commoditized, presenting challenges and opportunities for those involved. The discussion also touches on the shift from cloud-based subscriptions to hardware ownership as a response to commoditization. Additionally, there are considerations about the storage and computing landscape, particularly regarding the efficiency of capital allocation. The passage raises significant questions about the impact of commoditization on both personal and large-scale computing, emphasizing the need for further analysis and collaboration to address these complex issues.
NVIDIA has historically excelled in developing software tailored to its hardware, which has been pivotal to its success and influence in the AI industry, much of which can be attributed to its proprietary CUDA technology. The importance of software in enhancing hardware capabilities is highlighted, prompting a reflection on the potential of working with open-source libraries like llama.cpp to further accelerate hardware performance. The writer expresses a willingness to operate at the intersection of software and hardware, an area they find intriguing, especially with tasks like memory management and massively parallel processing on machine learning inference cards. This convergence of software and hardware is not only a subject of professional interest for the author but also recognized as an area of important technological development.
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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.