Tenyks’ Post

DynamoLLM: An Energy-Management Framework for Sustainable Artificial Intelligence Performance and Optimized Energy Efficiency in Large Language Model (LLM) Inference https://lnkd.in/dzvugR48 Practical Solutions for Energy-Efficient Large Language Model (LLM) Inference Enhancing Energy Efficiency Large Language Models (LLMs) need powerful GPUs to process data quickly, but this consumes a lot of energy. DynamoLLM optimizes energy usage by understanding distinct processing requirements and adjusting system configurations in real-time. Dynamic Energy Management DynamoLLM automatically rearranges inference clusters to optimize energy usage while meeting performance requirements. By monitoring the system’s performance and adjusting configurations as needed, it finds the best trade-offs between computational power and energy efficiency. Performance and Environmental Impact DynamoLLM can save up to 53% of the energy needed by LLM inference clusters, reducing consumer prices by 61% and operational carbon emissions by 38%, while maintaining required latency Service Level Objectives (SLOs). Value of DynamoLLM DynamoLLM significantly improves the sustainability and economics of LLMs, addressing financial and environmental concerns in the field of Artificial Intelligence. AI Solutions for Business Transformation Utilize DynamoLLM to enhance your company’s AI capabilities, ensuring sustainable performance and optimized energy efficiency in LLM inference. Explore how AI can revolutionize your business by identifying automation opportunities, defining KPIs, selecting suitable AI solutions, and implementing them gradually. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com and stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. Discover how AI can redefine your sales processes and customer engagement at itinai.com. #DynamoLLM #EnergyEfficiency #AI #Sustainability #BusinessTransformation #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

Messy vs Clean Data Does it matter now? The short answer is still yes However.... Things are changing with Agentic AI... Throughout the past several years, there has been a resounding focus from leaders in Data, IT, and the AI space to make sure your data is clean, organized, and consolidated. The reasoning was, "AI is coming." In effort to see value with AI, your data and repositories would need to mature. Unfortunately, not everyone responded with the same agility and urgency to these calls. This has made it difficult to produce value with generative ai leveraging messy data. These organizations are playing "catch up" to enjoy the productivity, cost reductions, and transformation that AI promises to bring. Fortunately, more recent development's in AI architecture are changing the paradigm. Enter Agentic AI architecture's... These reasoning engines allow for multi-turn, multi-step, and multi-pluggin requests to be completed. This happens regardless of workflows, dialogue trees, and even messy data irrelevant to location. What's needed to solve problems with messy data is a more robust reasoner that understands what systems, resources, and calculations need to be made inherent to the core AI engine. So who understands this? The CEO's of NVIDIA, Meta, Box and Moveworks have their teams focused on the next generation of Gen AI architecture.. Agentic AI! How have you approached a more scalable and robust architecture in your AI projects? #AIarchitecture #AgenticAI #AIAgents #Automation

🚀 A New Era in Application Development: AI-Driven Generation on the Fly As AI continues to evolve, we are witnessing a transformative shift in the way applications are developed and personalized. No longer confined by static codebases or lengthy development cycles, AI-driven application generation is offering tailored, real-time solutions for users across industries. 💡 Key Technologies Leading This Transformation: Neural Models for real-time interactions and adaptability Generative AI for creating personalized applications based on user input Diffusion Models that enhance predictive, seamless user experiences With these advancements, businesses can: ✅ Reduce development time and costs ✅ Improve user engagement through dynamic, responsive applications ✅ Scale software solutions that evolve with user needs 🔮 Predictions: By 2030, we'll see AI-generated applications as the norm, leading to the rise of hyper-personalized digital ecosystems and on-the-fly business solutions. We’re at the dawn of a new era in software development—one where AI can build and maintain applications in real-time. The future of tech has never been more exciting! #AI #AppDevelopment #TechInnovation #SoftwareEngineering #FutureOfWork #GenerativeAI

OPEA: OPEN PLATFORM FOR ENTERPRISE AI Building a Modular Framework for Enterprise GenAI Solutions Open source projects have rapidly advanced AI innovation, leading to the emergence of generative AI (GenAI). However, the rapid development has caused a split in techniques and tools, complicating its adoption in businesses and creates unnecessary complexity in deployment. Intel Corporation and other industry leaders including Hugging Face have launched the Open Platform for Enterprise AI (OPEA) to address these challenges by building a detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines. The OPEA platform also includes: 🔹 Architectural blueprints of retrieval-augmented generative AI component stack structure and end-to-end workflows 🔹 A four-step assessment for grading generative AI systems around performance, features, trustworthiness and enterprise-grade readiness The modular architecture illustrated in the image below includes: 🔹 GenAI models – large language models (LLMs), large vision models (LVMs), multimodal models, etc.    🔹Ingest/data processing  🔹Embedding models/services  🔹Indexing/vector/graph data stores  🔹Retrieval/ranking  🔹Prompt engines  🔹Guardrails  🔹Memory systems As this project progresses, I will be keenly observing how OPEA tackles essential elements such as Policy and Security, which are crucial for enterprise solutions and effective AI governance. 🔗 For more information about the project including Github Repo please check the links in the comments. #ArtificialIntelligence #RAG #Opensource

🤖 Why a GPT Workstation? Our GPT workstation is designed to serve as a sandbox for testing and developing applications using generative AI models like GPT. Whether you're a data scientist, a developer, or an AI enthusiast, this workstation will provide the tools and environment necessary to push the boundaries of what's possible. 🛠️ What We're Building: State-of-the-Art Hardware: Ensuring you have the computational power to train and deploy AI models efficiently. Collaborative Space: A platform for idea-sharing and collaborative problem-solving with peers and experts. Access to Leading AI Models: Including the latest versions of GPT and other frameworks to foster innovation. 🎯 Our Goal: To democratize access to advanced AI technologies and foster a community where innovation thrives. Whether you're looking to prototype a new product, enhance your existing solutions, or simply explore the potential of AI, our workstation will be your gateway. 🔗 Want to be part of this journey? We're looking for collaborators, beta testers, and AI pioneers who want to make a mark. Reach out to learn more about how you can get involved and help shape the future of AI! #ArtificialIntelligence #GPT #Innovation #Technology #Collaboration #POC https://lnkd.in/eRe7CpXP

🚀 Unlock the Power of AI with Fabric: Your Ultimate Productivity Tool! 🚀 🎉 I'm thrilled to share fabric, an innovative #AI tool designed to boost productivity and streamline your workflow! Whether you're a #student, #professional #developer, or #tech enthusiast, #Fabric can transform how you interact with #AI, making it more accessible and efficient. Here's a breakdown of what #Fabric offers: 📚 Key Features: 🛠️ Open-Source & Crowdsourced: Access a library of prompts and patterns to interact with various AI models. 💻 Command Line Interface: Enjoy a seamless CLI-native experience for easy AI integration. 🔌 Seamless Integration: Simplify the process of incorporating AI functionalities into your programs. 🌐 Flexible Access: Use AI models locally or remotely, thanks to tools like Twin Gate for fast and secure connections. 🔍 Advanced Functionalities: Create custom patterns for specific tasks, enhancing productivity and personalization. 📑 Efficient Content Management: Organize and summarize vast amounts of content for better learning and decision-making. 🧠 Critical Thinking & Deep Analysis: Balance AI automation with intentional consumption to enhance understanding and growth. 🕒 Setup Process: 1) Install necessary tools like PIP x. 2) Configure API keys for OpenAI and Anthropic. 3) Start leveraging AI models locally or remotely with ease. 🔗 Link for Fabric Resources :- https://lnkd.in/gKeXNdpQ Join me in embracing #AI to supercharge your productivity and learning! Share this with your #network to help others benefit from #Fabric's powerful features.🌐🌐 #AI #Productivity #TechInnovation #DeveloperTools #OpenSource #AIIntegration #Learning #TechCommunity #Automation #DataDriven #PersonalGrowth #Efficiency

🌟 𝗧𝗵𝗲 𝗔𝗜 𝗛𝘆𝗽𝗲: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Artificial Intelligence is reshaping industries and revolutionizing how we work, communicate, and innovate. But as the excitement around AI grows, it's crucial to remember that not all challenges can be addressed with prompt engineering alone. While prompt engineering plays a significant role in harnessing AI's potential, it is not a silver bullet for every problem. True innovation lies in: ➜ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: Real-world issues require more than AI prompts; they need robust data management, ethical considerations, and the integration of human expertise. ➜ 𝗔𝗜 𝗮𝘀 𝗮 𝗖𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁: AI's strength is in complementing human skills, not replacing them. It can enhance creativity, critical thinking, and decision-making, bridging the gap between AI potential and actual software engineering solutions. ➜ 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝘆𝗻𝗲𝗿𝗴𝘆: The boundary between AI and software engineering is thin, where software engineering provides the foundation for AI systems to be scalable, reliable, and effective. ➜ 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗨𝘀𝗲: Implementing AI responsibly involves ethical decision-making and aligning AI's capabilities with broader organizational goals. As we explore AI's possibilities, let's be mindful of its limitations and work towards integrating AI insights with human expertise. The real power of AI lies in its ability to drive innovation and solve complex problems when used as part of a holistic strategy. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀? 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘄𝗲 𝗲𝗻𝘀𝘂𝗿𝗲 𝗔𝗜 𝗶𝘀 𝘂𝘀𝗲𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆 𝗮𝗻𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆? #AIHype #ArtificialIntelligence #PromptEngineering #AIInnovation #FutureOfWork #TechStrategy #EthicalAI #HumanCenteredAI #AIIntegration #MondayBlues

🚀 Exciting News from Polyphasic Developers Ltd.! 🚀 We're at the forefront of a revolution, merging AI and Machine Learning with software development to unlock new possibilities and enhance efficiency. From automating tasks to creating smarter, more secure applications, we're leveraging cutting-edge technology to transform businesses worldwide. 💡 Dive into our latest blog post to explore how AI and ML are reshaping the software industry, driving innovation, and setting new benchmarks in customer service, analytics, and security. Discover our journey, the challenges we tackle, and the opportunities that lie ahead. 🔗 Read more about our adventures in AI and Machine Learning: https://lnkd.in/eiNfrZn6 Let's embark on this transformative journey together. Your thoughts and insights are welcome in the comments! #AIDevelopment #MachineLearning #SoftwareInnovation #PolyphasicDevelopers

In my Perspective: AI ecosystem is structured across multiple layers similar to development, each contributing uniquely to the development, deployment, and advancement of AI technologies. This structure can be visualized as an inverted pyramid, where the number of users and degree of specialization vary across each layer: SaaS Layer - Operational Users: At the widest part of the pyramid, these end-users leverage AI-powered Software-as-a-Service (SaaS) solutions to enhance their business operations without needing deep technical knowledge. Development Users: These users build AI-based applications using available tools, processes, and pre-trained models. They focus on integrating AI capabilities into practical applications. Library/Framework Builders: Professionals at this layer develop frameworks and libraries that simplify model building, agent creation, and core AI tasks, providing essential tools for developers. Core Model Builders: Entities like OpenAI and other foundational model creators develop large-scale AI models such as GPT-4, LLaMA, and other transformative technologies that power various AI applications. Algorithm Builders: Researchers and scientists working on the frontier of AI innovation, designing new algorithms and enhancing existing ones to push the boundaries of what's possible with AI. Computational Developers: These experts focus on the hardware and computational aspects of AI, including chip processing and optimizing hardware to support the intensive computations required by advanced AI models. Please feel free to comment if any of the layer can be added or removed with justifications #ArtificialIntelligence #AIEcosystem #AIDevelopment #AIFuture #MachineLearning #SaaS #TechInnovation #AIResearch #DeepLearning #AITechnology #AIFrameworks #AICommunity #TechIndustry #AIHardware #AIAlgorithms #Innovation