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

Yes, we will always need engineers. No, AI won’t replace them. Yes, we must adapt to the changes AI brings. No, we don’t need to master every detail about AI. Yes, we should focus on understanding AI’s general principles and concepts, prioritizing “integrability” and “usability” to create meaningful, impactful solutions. The focus must now shift to integrating AI-driven development practices, designing cloud-agnostic and multi-environment systems, building data-centric applications, and prioritizing resilience, scalability, and ethical AI practices. This is the era of the AI-augmented engineer. The definition: “AI-augmented engineering refers to the practice of using artificial intelligence tools, platforms, and algorithms to enhance the software engineering process. Instead of replacing engineers, AI acts as a powerful assistant, enabling faster, more efficient, and more innovative development across the software lifecycle” #AI #Engineering #Practices

Artificial Intelligence (AI) is revolutionizing software development by automating repetitive tasks, enhancing decision-making, and improving overall efficiency. AI-driven tools can analyze vast amounts of data to identify patterns and predict potential issues, enabling developers to make informed choices early in the project lifecycle. From code generation to automated testing, AI accelerates development processes and reduces the likelihood of human error. Furthermore, machine learning algorithms help optimize software performance by continuously learning from user interactions. As AI continues to evolve, its integration into software development will undoubtedly lead to more innovative solutions and streamlined workflows, empowering developers to focus on higher-level tasks and creativity. Learn more: https://lnkd.in/gY--9ChH #ArtificialIntelligence #SoftwareDevelopment #Innovation #MachineLearning #TechTrends

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

If you're building AI systems, you've probably felt overwhelmed by the endless array of tools and integrations available. 🛠️ Let's explore a cleaner architectural approach that's transforming how we think about AI system design. . . . Recently discovered an insightful piece by Cobus Greyling that revolutionized my understanding of AI system design: What are Functions? 🧠 Functions represent internal processes embedded within the LLM's decision-making core. Think of them as the AI's cognitive toolkit - integrated capabilities that allow the system to autonomously decide when and how to process information. What are Tools? 🛠️ Tools are external interfaces that connect AI systems to outside services (like APIs, databases, or search engines). They're pre-configured gateways that extend the AI's capabilities beyond its native knowledge. Modern AI Architecture: Functions Framework: • Internal decision engines • Contextual reasoning capabilities • Autonomous process management • Self-directed integration points Tools Framework: • External service connections • API orchestration • Defined interaction patterns • Real-time data access systems Engineering Principle: Optimal AI systems leverage both frameworks, but functions enable truly autonomous decision-making by embedding intelligence directly into the system's core. Technical Impact: This architecture enables sophisticated AI capabilities while maintaining system simplicity - a crucial balance in modern AI design. Learning from industry expertise has never been more crucial. Thanks to Cobus Greyling for this valuable perspective on LLM-driven decision making. Share your experiences with AI system architecture in the comments. Have you encountered challenges with tool integration vs. function implementation? Let's learn from each other's technical insights. 🔄 #ArtificialIntelligence #SoftwareArchitecture #TechInnovation #AIEngineering

As much as AI is fascinating, the main bottleneck remains to be scaling issues and we've actually figured out a real pathway to making it obsolete with our "Patent Pending" technology. 🔄 Rethinking AI Infrastructure: Why the Future of AI is Distributed As AI capabilities expand, the industry faces a critical inflection point. Traditional centralized computing models are hitting natural limitations in processing power, energy consumption, and scalability. The future demands a new approach. Introducing WDNA (Web-Distributed Neural Architecture) - a groundbreaking framework that transforms existing web infrastructure into a collaborative AI processing network. 🌐 Key Innovation: - Leverages existing web infrastructure as computational nodes - Distributed processing across millions of participating websites - Dynamic load balancing and resource optimization - Zero need for massive data centers or specialized hardware 💡 Business Impact: - 90% reduction in computational overhead - Democratized access to enterprise-grade AI capabilities - Enhanced privacy through localized processing - Sustainable scaling without environmental compromise Instead of building bigger data centers, we're making smarter use of the internet's existing computational power. Think of it as the "sharing economy" model applied to AI processing - utilizing idle server capacity across the web to power next-generation AI capabilities. Real-world applications span from natural language processing to complex machine learning model training, all powered by this distributed network rather than centralized servers. The future of AI isn't about building bigger boxes - it's about thinking outside the box entirely. WDNA represents a paradigm shift in how we approach AI infrastructure, solving today's scaling challenges while preparing for tomorrow's innovations. #ArtificialIntelligence #Innovation #Technology #DistributedComputing #Sustainability #FutureOfAI [Currently in private beta with select partners - DM for partnership inquiries]

Artificial Intelligence is no longer just a buzzword—it's a game-changer in software development! Imagine cutting down development time by automating repetitive tasks, or enhancing code quality through intelligent error detection. AI is making all this possible and more. Think about the possibilities: AI can predict user behavior, allowing us to design interfaces that users love before they even know what they want. It can optimize performance by analyzing and adjusting code in real time, ensuring applications run smoothly and efficiently. The power of AI extends to data-driven decision-making. By sifting through vast amounts of data, AI helps us make smarter, more informed choices, from the initial design phase to final deployment. Machine learning algorithms can identify patterns and insights that were previously unimaginable, leading to innovative solutions and improved user experiences. At GVM Technologies, we're at the forefront of this AI revolution. Our team is leveraging AI to deliver cutting-edge software solutions that are not only efficient but also scalable and robust. We're excited to continue pushing the boundaries of what's possible with AI in development. Join us on this exciting journey and experience the future of software development with GVM Technologies. #AI #Innovation #TechRevolution #GVMTechnologies

Perpetuum Mobile - a hypothetical device that can operate indefinitely without an external energy source. 💡Let me introduce: Perpetuum Cognitum - a hypothetical device that can process and generate knowledge endlessly without an external source of information. 🚂In the pre-digital era, inventors dreamed of a motor that could run without the need for fuel. Mechanical force was king, so naturally, we hoped to harness it for free. Now, our focus has shifted to the realm of information and knowledge. But the hopes for an idealized machine remain the same. And now, we dream of an AI that improves itself. Let me be clear. We already have extremely powerful AI that is improving on an incredible scale. But that process includes: 📊Data from the real world, annotated by humans 🔄Reinforcement learning with human feedback 📋Meta-planning and architecture design from teams of engineers Self-reliance is an illusion. True progress comes from efficiently processing external information. Intelligence arises from interaction and exploration. If you want a true breakthrough, it would be AI learning from unlabeled data (such as real-world videos), not writing its own code. 🤔🚀🌌 #AI #AGI #Google #OpenAI #GPT #future #data #AIRevolution #FutureOfAI #MachineLearning #AIInnovation #KnowledgeEconomy #ArtificialIntelligence #SelfImprovingAI #TechPhilosophy #DataDrivenFuture