Everyone wants to talk about the future, but few can say they've built the one we're standing on now. >> DDN is at the forefront of this movement. DDN has been called AI's bestkept secret.>> DDN laid the groundwork long before AI had a name. You know, our journey started out in high performance computing. So, Nvidia and DDN found our friendship and our long-term partnership through that origin.For over two decades, we've powered supercomputers that mapped the human genome, modeled the climate, and trained a ...

明敏 发自 凹非寺 量子位 | 公众号 QbitAI 不靠囤算力，拿下数家大模型明星公司订单。 93年创始人掌舵的清华系计算创业公司，有点出其不意。 2023上半年，百模大战开启，模型预训练需求空前爆发，在算力焦虑下，囤积算力成为一种趋同性动作，更充裕的算力几乎就等于金额更高 的订单。 10亿、甚至50亿 ，诱惑非常大。 站在暴风眼最核心，创始人闫博文没有这么做。从技术角度出发，他知道未来算力一定会有闲置，疯狂囤算力对于一家技术公司而言似乎也不 够make sense。 而且从结果看， 这也不影响他拿大单 。 百度、Kimi 以及视频生成赛道顶尖玩家 生数科技 等，都选择与他们合作。 So，why？ 3次获得戈登·贝尔奖 是石科技创立于2021年，团队从国家超级计算无锡中心孵化而来，是国内最早将超算智算并行优化的技术进行产业化的团队之一。 创始人兼董事长闫博文 ，出生于1993年，毕业于清华大学，是清华计算机系博士后。主要研究方向包括计算机应用技术、高性能计算、并行 优化等。 博士期间，闫博文 参与了国家超级计算无锡中心项目 ，主要实现将CFD整体算法移植到国产超算"神威·太湖之光"上。 "神威·太湖之光" ...

Company Overview & HPC Leadership - Evident, a spin-off of ATOS, specializes in high-performance computing (HPC) solutions, including mainframes, processors, and high-speed networks [2] - The company positions itself as the largest HPC provider in Europe and a significant provider in Latin America and India [2][3] - Evident is a partner with DDN, utilizing DDN as its back end for most of its delivered systems [2] HPC & AI Integration - Evident is bringing HPC to AI, exemplified by a cluster of approximately 40 DGX units implemented in Ecuador for computer vision, processing 5 gigabytes per second of video stream from over 18,000 cameras [4][5] - The Ecuador system utilizes a 31 petabyte Luster file system and a 21 petabyte system based on S400 and X2, chosen for its fast failover and recovery capabilities [6] Storage Challenges & Solutions - The presentation highlights challenges related to storage, including inefficient small file storage (7KB) leading to performance issues [7][8] - Another challenge involved seismic image processing requiring 900 gigabytes of data reads and writes, which was addressed using local caching [9][10] Large Language Models (LLMs) & System Failures - Meta's model training, involving an 800 gigabyte model and a cluster of 25,000 GPUs (using only 16 initially), faced frequent failures (419 failures in 45 days, approximately one failure every 3 hours) [11][12] - Checkpointing is deemed a necessity due to the high failure rate of components (GPUs, interconnects, power supplies, software bugs) in large-scale systems [13][14] Scalable & Efficient HPC Systems - Evident is building its first exascale system based on its technology, aiming for high efficiency, with one model ranking number one and another in position six on the Green500 list [15] - The company is delivering approximately five exascale systems in Brazil, utilizing DDN storage solutions focused on IOPS, bandwidth, and space [16][17] Key Takeaways for Large Workloads - Large workloads like LLMs require large systems, which are prone to component failures [17][18] - Mitigating failures requires a robust design in network and storage, compatible with the number of GPUs, and the use of checkpointing [18][19]

Gryphon Digital Mining, Inc. (NASDAQ:GRYP) Q4 2024 Earnings Conference Call March 31, 2025 4:30 PM ET Company Participants Steve Gutterman - Chief Executive Officer Sim Salzman - Chief Financial Officer Conference Call Participants Kevin Dede - H.C. Wainwright Jon Hickman - Ladenburg Thalmann Operator Greetings and welcome to the Gryphon Digital Mining Fourth Quarter and Full Year 2024 Earnings Call. On the call are Steve Gutterman, Chief Executive Officer of the company; and Sim Salzman, Chief Financial Of ...