Beth Kindig’s Post

The rise of generative AI and surging GPU shipments is causing data centers to scale from tens of thousands to 100,000-plus accelerators, shifting the emphasis to power consumption as a mission-critical problem to solve. I wrote about this last week in the analysis below. #NVDA #AMD #MSFT #GOOG #AMZN #AVGO Read more here: https://lnkd.in/eJbDC6Bs

'Big Tech is spending tens of billions quarterly on AI accelerators, which has led to an exponential increase in power consumption. Over the past few months, multiple forecasts and data points reveal soaring data center electricity demand, and surging power consumption. The rise of generative AI and surging GPU shipments is causing data centers to scale from tens of thousands to 100,000-plus accelerators, shifting the emphasis to power as a mission-critical problem to solve.' https://lnkd.in/gWfNyy9t

Super Micro Computer Inc. has reported a significant increase in its share price following a surge in quarterly GPU shipments, which reached an impressive 100,000 units. This boom is attributed to the growing demand for artificial intelligence technologies and applications. As AI continues to expand across various industries, companies like Super Micro are positioning themselves to meet this rising need. The developments underscore the ongoing transformation driven by AI and its impact on the technology sector. https://lnkd.in/ggTcYAnX #SuperMicro #GPUs #ArtificialIntelligence #AIBoom #TechIndustry #StockMarket #Innovation #TechnologyNews #QuarterlyResults #AIApplications #UnderstandingEnterpriseTech #EnterpriseTechnologyNow #EnterpriseTechnologyToday

Nvidia's flagship H100 GPU, introduced in 2022, quickly became the top choice for AI data center operators through 2023. The H100 capitalizes on GPUs' inherent advantage in parallel processing, allowing them to handle multiple tasks simultaneously with impressive throughput. These GPUs are equipped with significant amounts of onboard memory, which enhances their capability to train large AI models and perform AI inference tasks efficiently. The rapid adoption of AI applications is expected to spark a productivity surge globally, with the potential to generate up to $200 trillion in economic activity by 2030, according to projections by Cathie Wood's ARK Investment. Major tech companies, including Microsoft and Amazon, are investing heavily in AI GPUs, filling their data centers with them to rent out processing power to developers, who gain access to high-performance infrastructure without having to invest billions in hardware. This arrangement benefits everyone involved—tech companies and Nvidia profit from demand, while developers gain access to scalable AI resources at a fraction of the cost of building their own infrastructure. While Nvidia's H100 and its newer successor, the H200, remain highly sought after, the company's latest Blackwell architecture represents an unprecedented leap forward in performance. Blackwell-powered GB200 GPUs can conduct AI inference at speeds up to 30 times faster than the H100, enabling developers to tackle even more demanding AI workloads. Nvidia CEO Jensen Huang has indicated that individual GB200 GPUs will be priced similarly to the initial price range of the H100, around $30,000 to $40,000, highlighting Nvidia's commitment to pushing the boundaries of AI processing power while maintaining pricing at established levels for top-tier GPUs. https://lnkd.in/en_NB5ry

Super Micro shares surge as AI boom drives 100,000 quarterly GPU shipments - https://lnkd.in/gED_CycM #supermicro #aiservermaker #supportaifactories #100000gpusshipmentquarterly #liquidcoolingsolutionfeatured #liquidcoolingsolutiondlc #newdlcproductintroduction #highestgpuperrackdensity #keydifferentiator #energysavingspacesavingforaiinfrastructuredeployment

💡 As the demand for AI and machine learning continues to grow, powerful computational resources like the NVIDIA H100 GPU have never been more critical. In this blog article, we address several key factors to ensure you're making the best choice for your AI workloads. 🎯 Contact us to learn more about our GPU rental options and how we can help you achieve your AI development goals. #AI #NVIDIA #H100 #GPU #Guide

New Nvidia Blackwell B200 GPU (4X boost in training, up to 30X faster inference, 25X more energy efficiency).. Designed for trillion-parameter AI datasets, paving the way for advanced computing solutions and next generation of multimodal AI tech #artificialintelligence #cloudcomputing #generatieveai #airevolution

Facing a decision between using NVIDIA H100 and A100 GPUs for your AI/ML models? Explore this in-depth comparison of H100 vs. A100 GPUs. Uncover the key performance differences, cost considerations, and real-world applications to make the best choice for your needs! Dive in -> https://lnkd.in/dHVWtYMj

NVIDIA continues to push the boundaries in generative AI, highlighted by their latest achievements in the MLPerf benchmarks. NVIDIA TensorRT-LLM software has supercharged the inference process for large language models on NVIDIA Hopper Architecture GPUs, resulting in a 3x performance boost compared to six months ago. Leading organisations are already leveraging TensorRT-LLM to fine-tune their models 🚀 Learn more: https://bit.ly/3JelyFh #NVIDIA #ArtificialIntelligence #AI

Even a single B200 chip is extremely interesting for the future of GPU compute. Not only do we have the first compute chiplet design from Big Green, their newest interconnect is blazing fast -- over 10% faster than the *HBM* (main memory!) speed of the current-gen H100! That's not even to mention the 5x gain in the main memory bandwidth of the full B200 chip. The efficiency gain just from that is already staggering (5.0x bandwidth / 1.3x wattage).