Deepak Devjani’s Post

AI in IT infrastructure design. Let’s delve into the specifics: *AI Infrastructure Overview Definition: AI infrastructure refers to the hardware and software specifically designed to support AI workloads, including machine learning (ML) and deep learning tasks. Purpose: It enables efficient execution of AI applications, such as chatbots, facial recognition, and computer vision. Components: AI infrastructure includes GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), high-speed networking, scalable storage, and specialized software tools. *Key Differences from Traditional IT Infrastructure: Processing Power: AI infrastructure relies on GPUs and TPUs for parallel processing, unlike traditional CPUs. Data Handling: It emphasizes scalable storage to manage large datasets inherent in AI applications. Cloud-Centric: AI infrastructure is tailored for cloud environments, favoring cloud-based solutions over on-premise data centers. *Factors Driving Successful AI Infrastructure Implementation: Data Storage and Management: Efficiently handle data for training and running AI models. Specialized Hardware: Leverage GPUs and TPUs for computational power. Software Stacks: Use ML libraries (e.g., TensorFlow, PyTorch) for model development and training.

Training AI models is about to become a luxury service for enterprises. NetApp’s Russell Fishman recently shared some interesting insights about show AI will change how companies manage their storage and IT infrastructure, highlighting: - That AI training will become a specialized service that companies rent from companies due to the high electrical power and money the associated GPUs demand - What it’ll take to build user-facing AI services - How NetApp’s intelligent data infrastructure can help with improving access for AI applications, training, retrieval-augmented generation and more It’s worth the read: https://lnkd.in/etKrPfVg  #AI #BlueXP #EnterpriseStorage

Can AI Scaling Sustain Its Momentum Through 2030? An In-Depth Analysis Epoch AI's report assesses whether AI scaling can continue at its current pace through 2030. Key challenges include increasing demands on compute power, chip manufacturing bottlenecks, limited data availability, and latency. Additionally, economic constraints and infrastructure investments will play pivotal roles in determining AI's future. Despite these hurdles, with continued innovation and investment, massive advancements in AI model training are likely within reach. Key Topics: Compute Power Constraints: AI scaling demands immense energy, requiring sustainable solutions and energy-efficient technologies to meet future needs. Chip Manufacturing Bottlenecks: The AI industry is heavily dependent on cutting-edge chips (e.g., GPUs, TPUs), but the semiconductor supply chain faces significant limitations. As models scale, the demand for specialized chips will continue to grow, putting pressure on production capabilities. Data Availability and Quality: Large AI models require massive datasets, but the availability of high-quality and diverse data may become a limiting factor. Addressing data bottlenecks will be crucial to future scaling. Latency and Network Infrastructure: High-performance AI systems rely on low-latency, robust networks for efficient data flow. Upgrades in infrastructure, including faster pipelines and optimized network designs, will be critical to avoid bottlenecks. Economic and Investment Decisions: While the technical feasibility of AI scaling exists, the economic realities—such as rising costs in energy, hardware, and maintenance—could determine how far AI scaling will advance. Strategic investments in hardware, data infrastructure, and power solutions will shape the future. https://lnkd.in/dhg8RQrJ #AI #FutureTech #ScalingAI #MachineLearning #ChipManufacturing #DataScience #AIInvestment #TechGrowth #DeepLearning #AI2030

To lead in developing AI means leading investments in hardware infrastructure. Hardware infrastructure plays an important role in AI’s future. Meta sharing details on two versions of our 24,576-GPU data center scale cluster. These clusters support our current and next generation AI models, including Llama 3, the successor to Llama 2, our publicly released LLM, as well as AI research and development across GenAI and other areas. These two AI training cluster designs are a part of our larger roadmap for the future of AI. By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100s as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s. https://lnkd.in/gjgsnuP6

Storlead Labs(Renice mother company), with its revolutionary iNc (In Nand Computing) technology, shines like a brilliant new star across the night sky, illuminating a new path for AI computing and leading us towards an unprecedented technological era. 🎉 🎠 ⬇ Storled Labcleverly utilized the physical characteristics and logical advantages of flash memory chips to innovatively develop iNc technology. INc technology embeds computing units directly into storage units, achieving on-site processing of data and greatly improving computing efficiency and energy consumption ratio. This innovative design has demonstrated astonishing performance of iNc chips in AI training and inference tasks. Opened up a new path for the field of AI computing. ⬇ Double Leap in Performance and Cost: Reshaping the AI Economy Landscape In terms of cost, iNc technology has also demonstrated astonishing advantages. The high price of traditional GPU servers has deterred many small and medium-sized enterprises. And iNc chips greatly reduce manufacturing costs by optimizing computing modes. It is estimated that the cost of an AI solution built using iNc technology is less than one-third of that of traditional GPU servers. This cost advantage makes AI technology no longer the exclusive domain of large enterprises, but an innovative tool that all enterprises can easily embrace. ⬇ New Defense Line for Privacy and Security: The in memory computing feature of iNc technology provides a new solution to this problem. By completing computing tasks directly at the data source, iNc chips avoid the risk of data leakage during transmission. This' data doesn't go out 'computing model has built an unbreakable defense line for users' sensitive data. According to security experts' assessment, the data leakage risk of systems using iNc technology can be reduced to less than one percent of traditional systems ⬇ Market potential and industry impact: opening a new chapter in edge AI With the rapid development of Internet of Things, 5G and other technologies, edge computing is gradually becoming a new blue ocean of AI applications. The iNc technology of Storlead Lab, with its unique technological advantages and market prospects, is gradually unlocking the infinite possibilities of edge AI. From autonomous driving to smart homes and industrial Internet of Things, iNc technology has demonstrated extensive application potential in various fields. It is predicted that by 2025, the global edge AI market will exceed the $100 billion mark. With its iNc technology, Storlead Lab is expected to occupy an important position in this market and lead the industry's development.

Kneron launches new Edge AI chip at COMPUTEX 2024 Kneron released its latest Edge AI server and an AI-embedded PC. The KNEO 330, Kneron’s latest and second private Edge GPT server, follows its first unit, the KNEO 300, launched in 2023. The KNEO 300 already has enterprise customers in manufacturing, financial services, and universities, including Stanford and UCLA. The KNEO 330 offers 48 TOPs of AI computing power, up to eight concurrent connections, and supports LLMs and stable diffusion. Its RAG accuracy is comparable to Cloud solutions under much lower hardware conditions. The KNEO 330 reduced the overall cost of AI for small enterprises by 30%-40%. Read more 👉 https://bit.ly/3yPQKc0 #ElectronicSpecifier #Engineering #Tech #AIChip #Computex #Electronics

I'm thrilled to share that NetApp is revolutionizing storage for AI with its intelligent data infrastructure! As AI inferencing becomes widespread, accessing vast amounts of connected data is crucial, and NetApp's offering is perfectly aligned to meet this need. With the rise of GenAI large language models (LLMs), data silos are being torn down, enabling LLMs to access proprietary data and deliver accurate responses. NetApp's expertise in intelligent data infrastructure ensures seamless data movement, adhering to corporate data governance policies. Russell Fishman, NetApp's Senior Director of Product Management for AI, highlights the evolving landscape of AI training and inferencing. While AI training becomes specialized and restricted to a few providers, AI inferencing enters the value phase, democratizing AI across industries. NetApp's intelligent data infrastructure, built on the data fabric concept, offers unparalleled capabilities. With extensive OEM support, connections to various servers, and a robust data estate, NetApp enables efficient data management and access. Join us in celebrating NetApp's vision of intelligent data infrastructure, where storage is optimized for AI. Let's embrace the power of AI and unlock new possibilities together! #NetApp #AI #DataInfrastructure #GenerativeAI #DataGovernance https://lnkd.in/gxAfTXFs

MLPerf® Training 4.0 benchmarks are now live. We join industry leaders such as NVIDIA, Dell Technologies, Hewlett Packard Enterprise, Supermicro, and Google Artificial Intelligence benchmarking performance. Most importantly, we've release the world’s first MLPerf® certified Power Consumption benchmarks. David Kanter Executive Director of MLCommons "Sustainable Metal Cloud’s release establishes a baseline for best practice power consumption. As a community, it’s important that we can measure things so we can improve them. I hope SMC’s initial results will help drive transparency around the power consumption of machine learning training.” https://lnkd.in/gqDtF7gC #MLPerf #AI #AITraining #benchmarks

SMC - Sustainable Metal Cloud MLPerf power results are not only a world first they set the standard by which others will be judged on AI power saving. The underlying Firmus liquid immersion cooling platform supports next generation sustainable AI compute.