Nisha Talagala

Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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Challenges of AI Networking for hyperscalers and enterprises Sept 27, 2024 I had the opportunity to meet with Manohar Paluri, VP of AI at Meta, this evening at the AI Tech Leaders Club, just under 24 hours before the AI Infra Summit on September 27, 2024. It’s been confirmed that the large training cluster at 500K GPUs is the next frontier for the hyperscaler industry. Meanwhile, on the enterprise side, standalone cluster sizes typically range from 20K to much smaller setups for retraining and inference. From my experience, while the challenges in these two sectors are intertwined, they can still be stated separately. 1) Checkpointing Challenges: Large GPU clusters require numerous checkpoints to maintain system reliability. The industry has recently implemented networking and checkpointing solutions for clusters with around 20K GPUs. Imagine the challenges we’ll face at the scale of 500K GPUs! 2) GPU Utilization: Typical GPU utilization hovers around 20%. While hyperscalers may achieve better numbers, overall utilization remains low. For this, a startup came up a clever solution to power down the idle GPUs dynamically to save up to 60% of power consumption from a GPU cluster. This approach could either reduce power usage from idle GPUs or, in conjunction with increased GPU utilization through further resources sharing—like memory, network, and model partitioning/parallelization—enhance overall efficiency. 3) Deployment Flexibility: The deployment of data center networks (hyperscaler cloud) or private networks (on-premises) for enterprises often lacks the necessary flexibility. For more insight, I recommend reading the justification for Google’s Optical Circuit Switching (OCS) network. Following conventional practices without innovative solutions can lead to over-investment in AI and cloud infrastructure. 4) ROI Optimization: Classic cloud infrastructure is often not optimized for ROI in AI networking, which leads to operational challenges for enterprises. To understand this, a little involved discussion is needed. Once understood, it becomes part of your instinct. Consider the competitive disadvantage faced by businesses that optimize ROI in their AI networks. Free Takeaway from me: Conversations about the challenges of AI infrastructure and networking are very welcome at the AI Infra Summit, and participants will receive the latest AI networking problem slide! 😊

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Check out Tejas Kumar's deep dive into how computers work. It is insightful and refreshing, helping me revisit and connect those concepts taught in CS. Give it a watch: https://lnkd.in/gHHftZmH

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Check out this new SIGMOD '24 scientific paper. Our customers love Amazon MemoryDB for its low latency performance, rich flexible API, large scale (up to 500 nodes per cluster), security, availability (4 9s SLA), and AWS's operational excellence. We designed MemoryDB for 11 9s of durability while still providing in-memory performance. My team created a nice technical scientific paper on MemoryDB's architecture. If you would like to learn about what's under the hood, check out this SIGMOD '24 paper. Yacine Taleb, Xingbo Wang, Nan Yan, Allen Samuels, Kevin McGehee, Stefan Mueller, and Jean G. - excellent work publishing this paper! https://lnkd.in/gFXPvci8 #aws #memorydb #inmemory #databases #durability #science #sigmod

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🌟⚡🛰️📱🖥️ Jensen Huang to Visit India Among Elite Hosts for Nvidia AI Summit🖥️📱🛰️⚡🌟 🌟Nvidia’s CEO, Jensen Huang, will be visiting India to attend the company’s flagship AI conference, the Nvidia AI Summit. This event will take place from October 23 to 25, 2024, at the Jio Convention Centre in Mumbai. Notably, India is one of only three countries worldwide, along with the United States and Japan, to host this prestigious summit. ⚡ Technological Advancements in Chip Manufacturing and AI Talent Development 🌟Current Scenario Nvidia's CEO Jensen Huang's visit to India highlights the country's growing importance in the tech industry. During his last visit, discussions with Prime Minister Narendra Modi focused on India's potential as a hub for chip manufacturing and AI talent development. 🌟Chip Manufacturing The semiconductor industry is experiencing a surge in demand due to the rise of AI applications. Companies are investing heavily in expanding data centers and semiconductor fabrication plants (fabs) while exploring advancements in chip design, materials, and architectures. AI is playing a crucial role in optimizing chip architectures, reducing power consumption, and increasing performance. 🌟AI Talent Development India is poised to become a significant player in AI talent development. The country is focusing on enhancing its educational infrastructure to produce skilled AI professionals. Initiatives are being taken to bridge the talent gap in the semiconductor industry by leveraging AI to tackle complex manufacturing challenges. This includes developing AI capabilities in engineering, design, and factory operations. ⚡Future Perspectives: 1. Increased Investments: Expect significant investments in AI and semiconductor manufacturing to continue, with a focus on creating hyper-automated intelligent factories. 2. Educational Initiatives: Strengthening educational programs to produce a skilled workforce capable of driving AI and chip manufacturing innovations. 3. Global Collaboration: Enhanced collaboration with global tech leaders to bring cutting-edge technologies and best practices to India. 🌟This summit underscores India’s growing role in the global tech landscape.📱 ⚛️ 💻 ✈️🚀🏍️🛰️ 📡🌍 #NvidiaAISummit #ChipManufacturing #AITalentDevelopment #TechInnovation #IndiaTech #JensenHuang #MumbaiEvent #AIConference #GlobalTechSummit #NarendraModi

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This is our latest article on DZone, co-written with Giovanni Cuccu. We discussed a solution for keeping two master databases aligned when they are provided as a requirement. https://lnkd.in/dBgaNCdm

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We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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🎙️ Exciting news from the world of AI and technical documentation 🤖 Join us on September 4th at 1:00 PM for a special episode of NetApp ONAIR where we'll be diving into the transformation of our support site over the summer. 🌞 Our guest, Grant Glass, PhD, a Data Scientist at NetApp, will share insights on how we're using AI to enhance our support response capabilities. 🚀 Tune in to discover the behind-the-scenes of building "DOC" Support Bot and get your questions answered live! 🎧 #NetApp #AI #TechnicalDocumentation #TechPodcast #DataScience #NetAppONAIR

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Vincent Fu, in collaboration with ankit kumar and Arun George, has another interesting open ecosystem focused FIO blog post. You can learn about Flexible Data Placement (FDP) and how you can emulate and test FDP functionality using ecosystem projects. It serves as a great guide to getting started and we hope it helps inspire additional integrations and use cases. Take a look here:

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For those interested in how cloud scale database systems work, below is a deep dive on Amazon MemoryDB. MemoryDB delivers microsecond-level read latencies, and throughput in the hundreds of millions of queries per second, while offering multi-AZ durability with automatic failover.

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The 7th workshop on Neural Scaling Laws is about to start: https://lnkd.in/gQSB5k8v Scaling laws, emergency, alignment, transfer learning, with a focused track on multilingual models. Looking forward to kick it off with Irina Rish and discuss all these topics with Yuhai Tu, Paul Bogdan, Hailey Schoelkopf, Darshil Doshi, Rio Yokota, Neha Sengupta, Preslav Nakov, Tatiana Shavrina, PhD, Yishi Xu. Join in person or virtually! #icml2024 #ai

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Fascinating conversation.. The question is what percentage of future workloads will be running on HPC infrastructure vs customized CPU based server infrastructure.

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Attend the webinar (or watch recording) to get an introduction to LLM inference and NVIDIA NIMs along with a demo. Learn more about #haystack and deepest Link: https://lnkd.in/eCaGY7MJ #llms

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An interesting alternative emerges to transformer-based architectures, and here comes a new acronym - the LFMs...MIT spinoff Liquid debuts non-transformer AI models and they’re already state-of-the-art https://bit.ly/3TQdvnO #govtech #AI #genAI

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