Google Cloud has expanded its AI infrastructure with new offerings focused on speed and scale. The A3 VMs, based on Nvidia H100 GPUs, are designed for large language models and generative AI training and inference, providing significantly improved performance compared to previous generations. Google is also improving networking infrastructure with the introduction of Cross-Cloud Network platform, allowing easier and more secure connections between Google Cloud and on-premises environments. Furthermore, Google Cloud is enhancing data and storage capabilities with updates to Cloud Storage and Dataproc Spark, boosting data access speeds and enabling faster processing for AI workloads.
This post details how to train a large language model (LLM) comparable to OpenAI's GPT-3 175B parameter model, nicknamed "O1," for under $450. Leveraging SkyPilot, a framework for simplified and cost-effective distributed computing, the process utilizes spot instances across multiple cloud providers to minimize expenses. The guide outlines the steps to prepare the training data, set up the distributed training environment using SkyPilot's managed spot feature, and efficiently train the model with optimized configurations. The resulting model, trained on the Pile dataset, achieves impressive performance at a fraction of the cost typically associated with such large-scale training. The post aims to democratize access to large language model training, enabling researchers and developers with limited resources to experiment and innovate in the field.
HN users generally express excitement about the accessibility and cost-effectiveness of training large language models offered by SkyPilot. Several commenters highlight the potential democratizing effect this has on AI research and development, allowing smaller teams and individuals to experiment with LLMs. Some discuss the implications for cloud computing costs, comparing SkyPilot favorably to other cloud providers. A few raise questions about the reproducibility of the claimed results and the long-term viability of relying on spot instances. Others delve into technical details, like the choice of hardware and the use of pre-trained models as starting points. Overall, the sentiment is positive, with many seeing SkyPilot as a valuable tool for the AI community.
SoftBank, Oracle, and MGX are partnering to build data centers specifically designed for generative AI, codenamed "Project Stargate." These centers will host tens of thousands of Nvidia GPUs, catering to the substantial computing power demanded by companies like OpenAI. The project aims to address the growing need for AI infrastructure and position the involved companies as key players in the generative AI boom.
HN commenters are skeptical of the "Stargate Project" and its purported aims. Several suggest the involved parties (Trump, OpenAI, Oracle, SoftBank) are primarily motivated by financial gain, rather than advancing AI safety or national security. Some point to Trump's history of hyperbole and broken promises, while others question the technical feasibility and strategic value of centralizing AI compute. The partnership with the little-known mining company, MGX, is viewed with particular suspicion, with commenters speculating about potential tax breaks or resource exploitation being the real drivers. Overall, the prevailing sentiment is one of distrust and cynicism, with many believing the project is more likely a marketing ploy than a genuine technological breakthrough.
Summary of Comments ( 68 )
https://news.ycombinator.com/item?id=43639642
HN commenters are skeptical of Google's "AI hypercomputer" announcement, viewing it more as a marketing push than a substantial technical advancement. They question the vagueness of the term "hypercomputer" and the lack of concrete details on its architecture and capabilities. Several point out that Google is simply catching up to existing offerings from competitors like AWS and Azure in terms of interconnected GPUs and high-speed networking. Others express cynicism about Google's track record of abandoning cloud projects. There's also discussion about the actual cost-effectiveness and accessibility of such infrastructure for smaller research teams, with doubts raised about whether the benefits will trickle down beyond large, well-funded organizations.
The Hacker News post titled "Google Cloud Rapid Storage" linking to a Google Cloud blog post about AI supercomputers has a modest number of comments, focusing on a few key themes. No one directly discusses "Rapid Storage" which is curious given the HN post title. Instead, they discuss the overall strategy and implications of Google's AI infrastructure investments.
Several commenters express skepticism about Google's ability to compete effectively with NVIDIA in the AI hardware space. One commenter points out Google's history of entering and exiting markets, suggesting that their commitment to AI hardware may not be long-term. They question whether Google has the necessary focus and expertise to challenge NVIDIA's dominance. This sentiment is echoed by another commenter who highlights the challenges Google faces in catching up to NVIDIA's established ecosystem and software stack.
Another discussion thread revolves around the closed nature of Google's AI infrastructure. Commenters contrast this with the more open approach of other players in the market, arguing that a closed ecosystem limits innovation and collaboration. They suggest that Google's strategy might hinder the broader adoption of their AI technology.
The high cost of using Google's AI infrastructure is also mentioned. One commenter questions the affordability of these advanced resources, suggesting that they are primarily accessible to large corporations and research institutions, potentially leaving smaller players at a disadvantage.
Finally, some commenters express interest in the technical details of Google's AI supercomputer, particularly the networking technology and the performance of their custom TPU chips. However, the comments lack in-depth technical analysis, primarily focusing on high-level strategic considerations and market dynamics. There is a desire for more information, but the comments remain at a relatively surface level in terms of technical specifics.