Training large AI models like those used for generative AI consumes significant energy, rivaling the power demands of small countries. While the exact energy footprint remains difficult to calculate due to companies' reluctance to disclose data, estimates suggest training a single large language model can emit as much carbon dioxide as hundreds of cars over their lifetimes. This energy consumption primarily stems from the computational power required for training and inference, and is expected to increase as AI models become more complex and data-intensive. While efforts to improve efficiency are underway, the growing demand for AI raises concerns about its environmental impact and the need for greater transparency and sustainable practices within the industry.
A tiny code change in the Linux kernel could significantly reduce data center energy consumption. Researchers identified an inefficiency in how the kernel manages network requests, causing servers to wake up unnecessarily and waste power. By adjusting just 30 lines of code related to the network's power-saving mode, they achieved power savings of up to 30% in specific workloads, particularly those involving idle periods interspersed with short bursts of activity. This improvement translates to substantial potential energy savings across the vast landscape of data centers.
HN commenters are skeptical of the claimed 5-30% power savings from the Linux kernel change. Several point out that the benchmark used (SPECpower) is synthetic and doesn't reflect real-world workloads. Others argue that the power savings are likely much smaller in practice and question if the change is worth the potential performance trade-offs. Some suggest the actual savings are closer to 1%, particularly in I/O-bound workloads. There's also discussion about the complexities of power measurement and the difficulty of isolating the impact of a single kernel change. Finally, a few commenters express interest in seeing the patch applied to real-world data centers to validate the claims.
Google is allowing businesses to run its Gemini AI models on their own infrastructure, addressing data privacy and security concerns. This on-premise offering of Gemini, accessible through Google Cloud's Vertex AI platform, provides companies greater control over their data and model customizations while still leveraging Google's powerful AI capabilities. This move allows clients, particularly in regulated industries like healthcare and finance, to benefit from advanced AI without compromising sensitive information.
Hacker News commenters generally expressed skepticism about Google's announcement of Gemini availability for private data centers. Many doubted the feasibility and affordability for most companies, citing the immense infrastructure and expertise required to run such large models. Some speculated that this offering is primarily targeted at very large enterprises and government agencies with strict data security needs, rather than the average business. Others questioned the true motivation behind the move, suggesting it could be a response to competition or a way for Google to gather more data. Several comments also highlighted the irony of moving large language models "back" to private data centers after the trend of cloud computing. There was also some discussion around the potential benefits for specific use cases requiring low latency and high security, but even these were tempered by concerns about cost and complexity.
Storing data on the moon is being explored as a potential safeguard against terrestrial disasters. While the concept faces significant challenges, including extreme temperature fluctuations, radiation exposure, and high launch costs, proponents argue that lunar lava tubes offer a naturally stable and shielded environment. This would protect valuable data from both natural and human-caused calamities on Earth. The idea is still in its early stages, with researchers investigating communication systems, power sources, and robotics needed for construction and maintenance of such a facility. Though ambitious, a lunar data center could provide a truly off-site backup for humanity's crucial information.
HN commenters largely discuss the impracticalities and questionable benefits of a moon-based data center. Several highlight the extreme cost and complexity of building and maintaining such a facility, citing issues like radiation, temperature fluctuations, and the difficulty of repairs. Some question the latency advantages given the distance, suggesting it wouldn't be suitable for real-time applications. Others propose alternative solutions like hardened earth-based data centers or orbiting servers. A few explore potential niche use cases like archival storage or scientific data processing, but the prevailing sentiment is skepticism toward the idea's overall feasibility and value.
Microsoft has reportedly canceled leases for data center space in Silicon Valley previously intended for artificial intelligence development. Analyst Matthew Ball suggests this move signals a shift in Microsoft's AI infrastructure strategy, possibly consolidating resources into larger, more efficient locations like its existing Azure data centers. This comes amid increasing demand for AI computing power and as Microsoft heavily invests in AI technologies like OpenAI. While the canceled leases represent a relatively small portion of Microsoft's overall data center footprint, the decision offers a glimpse into the company's evolving approach to AI infrastructure management.
Hacker News users discuss the potential implications of Microsoft canceling data center leases, primarily focusing on the balance between current AI hype and actual demand. Some speculate that Microsoft overestimated the immediate need for AI-specific infrastructure, potentially due to inflated expectations or a strategic shift towards prioritizing existing resources. Others suggest the move reflects a broader industry trend of reevaluating data center needs amidst economic uncertainty. A few commenters question the accuracy of the reporting, emphasizing the lack of official confirmation from Microsoft and the possibility of misinterpreting standard lease adjustments as a significant pullback. The overall sentiment seems to be cautious optimism about AI's future while acknowledging the potential for a market correction.
Backblaze's 12-year hard drive failure rate analysis, visualized through interactive charts, reveals interesting trends. While drive sizes have increased significantly, failure rates haven't followed a clear pattern related to size. Different manufacturers demonstrate varying reliability, with some models showing notably higher or lower failure rates than others. The data allows exploration of failure rates over time, by manufacturer, model, and size, providing valuable insights into drive longevity for large-scale deployments. The visualization highlights the complexity of predicting drive failure and the importance of ongoing monitoring.
Hacker News users discussed the methodology and presentation of the Backblaze data drive statistics. Several commenters questioned the lack of confidence intervals or error bars, making it difficult to draw meaningful conclusions about drive reliability, especially regarding less common models. Others pointed out the potential for selection bias due to Backblaze's specific usage patterns and purchasing decisions. Some suggested alternative visualizations, like Kaplan-Meier survival curves, would be more informative. A few commenters praised the long-term data collection and its value for the community, while also acknowledging its limitations. The visualization itself was generally well-received, with some suggestions for improvements like interactive filtering.
Backblaze's 2024 hard drive stats reveal a continued decline in annualized failure rates (AFR) across most drive models. The overall AFR for 2024 was 0.83%, the lowest ever recorded by Backblaze. Larger capacity drives, particularly 16TB and larger, demonstrated remarkably low failure rates, with some models exhibiting AFRs below 0.5%. While some older drives experienced higher failure rates as expected, the data suggests increasing drive reliability overall. Seagate drives dominated Backblaze's data centers, comprising the majority of drives and continuing to perform reliably. The report highlights the ongoing trend of larger drives becoming more dependable, contributing to the overall improvement in data storage reliability.
Hacker News users discuss Backblaze's 2024 drive stats, focusing on the high failure rates of WDC drives, especially the 16TB and 18TB models. Several commenters question Backblaze's methodology and data interpretation, suggesting their usage case (consumer drives in enterprise settings) skews the results. Others point out the difficulty in comparing different drive models directly due to varying usage and deployment periods. Some highlight the overall decline in drive reliability and express concerns about the industry trend of increasing capacity at the expense of longevity. The discussion also touches on SMART stats, RMA processes, and the potential impact of SMR technology. A few users share their personal experiences with different drive brands, offering anecdotal evidence that contradicts or supports Backblaze's findings.
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.
Researchers have demonstrated the first high-performance, electrically driven laser fully integrated onto a silicon chip. This achievement overcomes a long-standing hurdle in silicon photonics, which previously relied on separate, less efficient light sources. By combining the laser with other photonic components on a single chip, this breakthrough paves the way for faster, cheaper, and more energy-efficient optical interconnects for applications like data centers and high-performance computing. This integrated laser operates at room temperature and exhibits performance comparable to conventional lasers, potentially revolutionizing optical data transmission and processing.
Hacker News commenters express skepticism about the "breakthrough" claim regarding silicon photonics. Several point out that integrating lasers directly onto silicon has been a long-standing challenge, and while this research might be a step forward, it's not the "last missing piece." They highlight existing solutions like bonding III-V lasers and discuss the practical hurdles this new technique faces, such as cost-effectiveness, scalability, and real-world performance. Some question the article's hype, suggesting it oversimplifies complex engineering challenges. Others express cautious optimism, acknowledging the potential of monolithic integration while awaiting further evidence of its viability. A few commenters also delve into specific technical details, comparing this approach to other existing methods and speculating about potential applications.
Building your own data center is a complex and expensive undertaking, requiring careful planning and execution across multiple phases. The initial design phase involves crucial decisions regarding location, power, cooling, and network connectivity, influenced by factors like latency requirements and environmental impact. Procuring hardware involves selecting servers, networking equipment, and storage solutions, balancing cost and performance needs while considering future scalability. The physical build-out encompasses construction or retrofitting of the facility, installation of racks and power distribution units (PDUs), and establishing robust cooling systems. Finally, operational considerations include ongoing maintenance, security measures, and disaster recovery planning. The author stresses the importance of a phased approach and highlights the significant capital investment required, suggesting cloud services as a viable alternative for many.
Hacker News users generally praised the Railway blog post for its transparency and detailed breakdown of data center construction. Several commenters pointed out the significant upfront investment and ongoing operational costs involved, highlighting the challenges of competing with established cloud providers. Some discussed the complexities of power management and redundancy, while others emphasized the importance of location and network connectivity. A few users shared their own experiences with building or managing data centers, offering additional insights and anecdotes. One compelling comment thread explored the trade-offs between building a private data center and utilizing existing cloud infrastructure, considering factors like cost, control, and scalability. Another interesting discussion revolved around the environmental impact of data centers and the growing need for sustainable solutions.
Enterprises adopting AI face significant, often underestimated, power and cooling challenges. Training and running large language models (LLMs) requires substantial energy consumption, impacting data center infrastructure. This surge in demand necessitates upgrades to power distribution, cooling systems, and even physical space, potentially catching unprepared organizations off guard and leading to costly retrofits or performance limitations. The article highlights the increasing power density of AI hardware and the strain it puts on existing facilities, emphasizing the need for careful planning and investment in infrastructure to support AI initiatives effectively.
HN commenters generally agree that the article's power consumption estimates for AI are realistic, and many express concern about the increasing energy demands of large language models (LLMs). Some point out the hidden costs of cooling, which often surpasses the power draw of the hardware itself. Several discuss the potential for optimization, including more efficient hardware and algorithms, as well as right-sizing models to specific tasks. Others note the irony of AI being used for energy efficiency while simultaneously driving up consumption, and some speculate about the long-term implications for sustainability and the electrical grid. A few commenters are skeptical, suggesting the article overstates the problem or that the market will adapt.
Austrian cloud provider Anexia has migrated 12,000 virtual machines from VMware to its own internally developed KVM-based platform, saving millions of euros annually in licensing costs. Driven by the desire for greater control, flexibility, and cost savings, Anexia spent three years developing its own orchestration, storage, and networking solutions to underpin the new platform. While acknowledging the complexity and effort involved, the company claims the migration has resulted in improved performance and stability, along with the substantial financial benefits.
Hacker News commenters generally praised Anexia's move away from VMware, citing cost savings and increased flexibility as primary motivators. Some expressed skepticism about the "homebrew" aspect of the new KVM platform, questioning its long-term maintainability and the potential for unforeseen issues. Others pointed out the complexities and potential downsides of such a large migration, including the risk of downtime and the significant engineering effort required. A few commenters shared their own experiences with similar migrations, offering both warnings and encouragement. The discussion also touched on the broader trend of moving away from proprietary virtualization solutions towards open-source alternatives like KVM. Several users questioned the wisdom of relying on a single vendor for such a critical part of their infrastructure, regardless of whether it's VMware or a custom solution.
Summary of Comments ( 294 )
https://news.ycombinator.com/item?id=44039808
HN commenters discuss the energy consumption of AI, expressing skepticism about the article's claims and methodology. Several users point out the lack of specific data and the difficulty of accurately measuring AI's energy usage separate from overall data center consumption. Some suggest the focus should be on the net impact, considering potential energy savings AI could enable in other sectors. Others question the framing of AI as uniquely problematic, comparing it to other energy-intensive activities like Bitcoin mining or video streaming. A few commenters call for more transparency and better metrics from AI developers, while others dismiss the concerns as premature or overblown, arguing that efficiency improvements will likely outpace growth in compute demands.
The Hacker News post titled "AI's energy footprint" discussing a MIT Technology Review article about the environmental impact of AI generated a moderate number of comments, exploring various facets of the issue. Several commenters focused on the lack of specific data within the original article, calling for more concrete measurements rather than generalizations about AI's energy consumption. They highlighted the difficulty in isolating the energy use of AI from the broader data center operations and questioned the comparability of different AI models. One compelling point raised was the need for transparency and standardized reporting metrics for AI's environmental impact, similar to nutritional labels on food. This would allow for informed decisions about the development and deployment of various AI models.
The discussion also touched upon the potential for optimization and efficiency improvements in AI algorithms and hardware. Some users suggested that focusing on these improvements could significantly reduce the energy footprint of AI, rather than simply focusing on the raw energy consumption numbers. A counterpoint raised was the potential for "rebound effects," where increased efficiency leads to greater overall use, negating some of the environmental benefits. This was linked to Jevons paradox, the idea that technological progress increasing the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource.
Several comments delved into the broader implications of AI's growing energy demands, including the strain on existing power grids and the need for investment in renewable energy sources. Concerns were expressed about the potential for AI development to exacerbate existing environmental inequalities and further contribute to climate change if not carefully managed. One commenter argued that the focus should be on the value generated by AI, suggesting that even high energy consumption could be justified if the resulting benefits were substantial enough. This sparked a debate about how to quantify and compare the value of AI applications against their environmental costs.
Finally, a few comments explored the role of corporate responsibility and government regulation in addressing the energy consumption of AI. Some argued for greater transparency and disclosure from companies developing and deploying AI, while others called for policy interventions to incentivize energy efficiency and renewable energy use in the AI sector. The overall sentiment in the comments reflected a concern about the potential environmental consequences of unchecked AI development, coupled with a cautious optimism about the possibility of mitigating these impacts through technological innovation and responsible policy.