AI Ecosystem’s Post

I'm excited to publish a new article on Medium 👉 https://lnkd.in/dU7Vn8V2 💲💲 The #cost of #cloud AI model inference is quite high and is a genuine concern for many organizations deploying #production-ready AI Apps. This cost is influenced by several factors, including the type of #hardware used (#GPUs, #CPUs, #TPUs), the size and complexity of the AI models (#multimodals), and the #volume of inference requests (#userbase). In this article, I play around with the idea of deploying in the #cloud a hypothetical #FinTech app with 1 million+ users, inferencing a large #multimodal model like OpenAI's #GPT-4o. The costs are immense! Luckily, the battle for faster and more cost-effective hardware is on with Google introducing Trillium 6th-Gen TPUs, NVIDIA planning to deploy the H200 GPUs in Q2 of 2024, and Groq's NPUs demonstrating remarkable capabilities better than GPUs in AI model inference. 🦜 Let us explore the #economics of production-ready AI deployment in this article 💡 Read, engage, and share your thoughts here 👉 https://lnkd.in/dU7Vn8V2

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You’ve heard that spot instances save money for front end apps. Did you know they would save you 70-80% of your GPU training costs vs. dedicated instances? If you use lots of model training hours, contact Faction. We can save you money and, at the same time, connect your data to all the clouds. Request a consultation at https://bit.ly/3VG9wvO or contact our Field CTO Jon Osborn directly for more information. #GPU #ModelTraining #AI #Technology

🌐🚀 Exploring the Future of Android with New Technologies and AI! 🤖📱 As Android developers, we’re standing at the intersection of incredible innovation and limitless potential. From advanced AI models to edge computing, the landscape is rapidly evolving, creating new ways to enrich user experiences and drive impactful solutions. 💡 Here’s how I see AI and new tech shaping Android development: 🔹 Personalization at Scale: AI enables tailored experiences, predicting user needs and enhancing engagement. 🔹 Seamless Automation: Automated testing, debugging, and even code generation—making development faster and more efficient! 🔹 Voice and Vision Capabilities: Integrated ML tools like TensorFlow Lite are enabling powerful features, from image recognition to advanced voice interfaces. 🔹 Enhanced Security: AI-driven threat detection and adaptive authentication are setting new standards for mobile security. Exciting times are ahead! How are you leveraging AI and new technologies in your Android projects? Let’s discuss! 💬👇 #Android #AI #MachineLearning #Innovation #TechTrends #AndroidDevelopment #FutureTech

How We Ran 11 AI Models Locally on Mobile We've developed a mobile app that needs to run inference from 11 AI models. Initially, we planned to run these models in the cloud and retrieve the results via API. However, to reduce dependency on the internet, we decided to run all the models locally. This approach brought up two significant challenges: App Size: Each model is about 50MB, resulting in an app size of around 600MB. Performance: Will the mobile be able to run all 11 models simultaneously without performance issues? The first challenge, the large app size, is something we have to accept. However, the second challenge directly affects the user experience, as the app might become slow or experience frame drops. Initially, our mobile device struggled to run more than five models simultaneously. Despite numerous optimizations, the same issue persisted, often causing the device to crash during testing. Our models are TFLite models that take an image as input and predict the output. Initially, we ran the computations on the mobile's GPU, but this led to performance issues. Switching to the mobile's CPU yielded amazing results. There were no frame drops, and performance was very smooth, even with animations in the UI thread to show the progress of the inference. Mobile CPUs are quite powerful, capable of performing inference on two models simultaneously without any noticeable frame drops in the UI thread. The total inference time ranges from 30 to 60 seconds, depending on the device's performance. We used Flutter for this mobile application, with everything written in Dart. Please let me know if you want to try out the application. Happy learning! 🚀🌟 💬 Share your thoughts in the comments; your feedback is valuable! ⬇️ 📍 Stay tuned for more data-driven insights. 📈🔍 #AI #MobileDevelopment #Flutter #MachineLearning #TFLite #TechInnovation

💡 Analyze earnings immediately on MODL<GO>. NVIDIA's the grand finale of big tech for the earnings season. The AI chipmaker is of interest to investors as they waited for results. Their data center arm saw 426.7% growth YOY, and ended up beating their estimate of $21.1 billion with $22.6 billion for the segment. This information was available within seconds of reporting on MODL <GO> on the Bloomberg Terminal and on the Bloomberg Professional Mobile App. 📱 #AI #NVIDIA

Early access rolling out - typepilot.app. This tool is not just must have tool on your android phone but better and more fun than any AI assistant out there. #ai #ml #android

Are you thinking of creating a co-pilot application using Retrieval Augmented Generation (RAG)? Refer to this notebook, a RAG-based co-pilot application in which everything is deployed locally (on your laptop or desktop). This kind of application is important for use cases, where one is dealing with sensitive data.

Missed out on Ryan Cunningham's keynote at the Power Platform Community Conference (#PPCC)? Watch this quick demo showcasing the intelligent capabilities of #PowerApps and #PowerPlatform which is about to hit us (if not already). Stay updated on the latest innovations in #Microsoft, #LowCode, #Copilot, and #AI. "... this is no ordinary Power Apps. It may be a beautiful Model Driven App with modern control, but it's more than that. This app is intelligent. It has a BRAIN."

Unlocking the Power of Local AI Copilots! 🚀 I recently came across this fascinating video that demonstrates how to develop customized Q&A chatbots locally using open-source tools and frameworks. Perfect for enterprises looking to leverage their proprietary data! 🔑 Key Takeaways: * Utilize Retrieval Augmented Generation (RAG) to connect large language models (LLMs) to your data * Convert data into vector embeddings using models like FAISS for efficient storage and retrieval * Orchestrate the workflow using frameworks like LangChain *Deploy locally on devices with smaller GPU footprints using optimized models like Llama 3 hosted on NVIDIA Inference Microservices (NIMS) Whether you're an AI enthusiast, developer, or business leader, this video provides a clear roadmap for creating powerful AI copilots that can help users access and navigate your enterprise's knowledge base effectively. Check out the full video here: https://lnkd.in/eszPQj6P I'd love to hear your thoughts! Have you experimented with similar approaches or tools? Let's discuss the potential applications and impact of locally deployed AI assistants. 🧠💡#AI #Chatbots #LocalAI #KnowledgeManagement #OpenSource #FAISS #LangChain #NIMS And yes, after watching the video I used perplexity to automate the post above by simply providing a link to the video.