This blog post details building a budget-friendly, private AI computer for running large language models (LLMs) offline. The author focuses on maximizing performance within a €2000 constraint, opting for an AMD Ryzen 7 7800X3D CPU and a Radeon RX 7800 XT GPU. They explain the rationale behind choosing components that prioritize LLM performance over gaming, highlighting the importance of CPU cache and VRAM. The post covers the build process, software setup using a Linux-based distro, and quantifies performance benchmarks running Llama 2 with various parameters. It concludes that achieving decent offline LLM performance is possible on a budget, enabling private and efficient AI experimentation.
This spreadsheet documents a personal file system designed to mitigate data loss at home. It outlines a tiered backup strategy using various methods and media, including cloud storage (Google Drive, Backblaze), local network drives (NAS), and external hard drives. The system emphasizes redundancy by storing multiple copies of important data in different locations, and incorporates a structured approach to file organization and a regular backup schedule. The author categorizes their data by importance and sensitivity, employing different strategies for each category, reflecting a focus on preserving critical data in the event of various failure scenarios, from accidental deletion to hardware malfunction or even house fire.
Several commenters on Hacker News expressed skepticism about the practicality and necessity of the "Home Loss File System" presented in the linked Google Doc. Some questioned the complexity introduced by the system, suggesting simpler solutions like cloud backups or RAID would be more effective and less prone to user error. Others pointed out potential vulnerabilities related to security and data integrity, especially concerning the proposed encryption method and the reliance on physical media exchange. A few commenters questioned the overall value proposition, arguing that the risk of complete home loss, while real, might be better mitigated through insurance rather than a complex custom file system. The discussion also touched on potential improvements to the system, such as using existing decentralized storage solutions and more robust encryption algorithms.
Summary of Comments ( 190 )
https://news.ycombinator.com/item?id=42999297
HN commenters largely focused on the practicality and cost-effectiveness of the author's build. Several questioned the value proposition of a dedicated local AI machine, particularly given the rapid advancements and decreasing costs of cloud computing. Some suggested a powerful desktop with a good GPU would be a more flexible and cheaper alternative. Others pointed out potential bottlenecks, like the limited PCIe lanes on the chosen motherboard, and the relatively small amount of RAM compared to the VRAM. There was also discussion of alternative hardware choices, including used server equipment and different GPUs. While some praised the author's initiative, the overall sentiment was skeptical about the build's utility and cost-effectiveness for most users.
The Hacker News post "Building a personal, private AI computer on a budget" (https://news.ycombinator.com/item?id=42999297) generated several comments discussing the feasibility, practicality, and implications of building a personal AI system.
Several commenters focused on the rapid advancements in the field, noting that the author's hardware recommendations might quickly become outdated. They highlighted how quickly the landscape changes in terms of both hardware capabilities and software optimizations. Some suggested that renting cloud GPU instances, despite the privacy trade-off, could be a more cost-effective approach in the long run given the rapid depreciation of hardware.
There was a discussion about the balance between cost and performance. Some questioned whether the proposed budget build would truly be powerful enough for meaningful AI tasks, particularly those involving larger language models (LLMs). Alternatives, like using a more powerful desktop or leveraging cloud resources, were discussed as potentially more practical options depending on the specific AI workloads intended.
Privacy was a central theme in the comments, reflecting the article's focus on a private AI solution. Commenters acknowledged the increasing privacy concerns associated with cloud-based AI and expressed interest in the possibility of maintaining control over their data. However, some pointed out the potential challenges of securing a personal AI system and the ongoing effort required to keep it up-to-date with security patches.
The difficulty of managing software dependencies and the complexity of setting up and maintaining a dedicated AI environment were also brought up. Commenters mentioned potential issues with CUDA drivers, library compatibility, and the general overhead involved in system administration.
Several comments explored alternative hardware configurations and approaches. Suggestions included using smaller, more efficient models, exploring different GPU options, and leveraging pre-built solutions like the NVIDIA Jetson platform for a more streamlined experience.
Finally, some commenters discussed the ethical implications of readily accessible personal AI, touching on potential misuse and the broader societal impact of powerful AI tools becoming more widely available. While excited about the possibilities, they also cautioned about the responsibilities that come with having such powerful technology at one's disposal.