From Weekend Flying to Cloud-Scale Thinking — Fixed-Wing Autonomy Meets GPU Simulation Last weekend’s “fun at the RC airfield” turned into something much bigger — a live experiment in AI-driven autonomy and GPU cloud simulation. I built and flight-tested a fixed-wing autonomous drone that flies without GPS, powered entirely by Visual SLAM and onboard AI inference running on a Jetson Orin. The aircraft perceives, maps, and self-corrects in real time — no satellite, no remote controller (I still have one just in case!) , just edge compute and algorithms doing their thing. But here’s the twist — all of the AI models, mapping logic, and flight profiles were trained, simulated, and stress-tested on Azure NC-series virtual GPUs (NVIDIA A10 v5) before deployment (If it was not a personal project I could do that on Parry's fully managed virtual work bench with GPU! which handles most of the provisioning automatically). That means I could run Isaac Sim and SLAM pipelines in a cloud GPU environment, validate flight scenarios, and push containerized models back down to the Jetson — a complete edge-to-cloud feedback loop from my home lab. 💡 Weekend Project Highlights: 🧠 Jetson Orin + TensorRT for onboard inference ☁️ Azure vGPU (A10 v5) for AI model training + simulation 🧭 Visual SLAM & AI perception stack (ROS 2 + Isaac SDK) 🚀 Fixed-wing platform tuned for GPS-denied flight autonomy 🔄 Real-time synchronization between cloud and physical flight tests This is kind of R&D — where edge devices meet GPU clouds, and a “weekend build” doubles as a live demo of scalable AI architecture. Projects like this remind me why I love working at the intersection of AI infrastructure, GPU systems, and autonomy — Thank you NVIDIA for building Jetson family of edge AI, Now we can build at global scale. #AIInfrastructure #EdgeAI #JetsonOrin #VisualSLAM #VirtualGPU #Azure #IsaacSim #NVIDIA #AutonomousSystems #WeekendEngineering #GPUComputing #MLOps #CloudArchitecture #PassionProject
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🔥 Big news in Vision AI — NVIDIA DeepStream SDK 8.0 is here! This is a major leap for real-time video analytics and edge-to-cloud AI systems. NVIDIA is clearly raising the bar for how AI sees and understands the world. DeepStream 8.0 brings a wave of next-gen capabilities: Multi-View 3D Tracking for synchronized, cross-camera object tracking. MaskTracker powered by advanced segmentation models for pixel-level precision Inference Builder that lets you deploy models straight from YAML configs — no manual scripting needed Full support for Blackwell GPUs, Jetson AGX Thor, and Ubuntu 24.04. This isn’t just an SDK update — it’s a statement. We’re entering an era where AI vision becomes more modular, scalable, and production-ready than ever If you’re building in smart cities, healthcare imaging, public safety, or industrial analytics, this release will shape your roadmap What are your thoughts on where video analytics is headed next? 👇 Let’s talk about it — edge, cloud, and everything in between #AI #DeepLearning #VideoAnalytics #NVIDIA #DeepStream #EdgeAI #ComputerVision #SmartCity #Innovation
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Huge opportunity! $100,000 in GPU credits! Applications for the Robotics & Physical AI Awards are now open! You can apply in any of the following categories: 1. Foundation models, robot brains, and runtime 2. Data engines, synthetic data, and simulation 3. Industrial robotics deployment and transformation 4. Vision AI and streaming video analytics 5. Platforms for benchmarking, visualization, and evaluation The grand prize winners will get $100,000 in high-performance AI compute on the Nebius AI Cloud, accelerated by NVIDIA AI infrastructure. You can find more information here: https://lnkd.in/e_kvVCA4
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AI has become deeply embedded in our daily lives. The next crucial step is optimization — reducing resource consumption without losing performance. The future will belong to those who can compress and adapt AI models for autonomous use on standard computing power — without relying on supercomputers or specialized GPUs. 👉 The race is not only about building bigger and smarter models, but about making them lighter and more efficient.
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🚀 The future of AI isn’t coming it’s already here, powered by NVIDIA. - NeMo Framework → Build and train state-of-the-art conversational AI. - DRIVE OS → The open SDK that accelerates autonomous driving, from perception to planning. - Triton Inference Server → Fast, scalable AI inference across GPUs and CPUs. Imagine deploying cutting-edge AI models seamlessly, teaching cars to think and react in real time, and building conversations that feel human all on one ecosystem. The AI revolution isn’t about one product. It’s about an ecosystem that works together. NVIDIA is making that possible. Curious to see where this will take us in the next 2 years? #AI #NVIDIA #NeMo #Triton #AutonomousDriving #GenerativeAI #DeepLearning #Innovation
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𝗧𝗵𝗲 𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗗𝗲𝗲𝗽𝗦𝘁𝗿𝗲𝗮𝗺 𝟴 𝗮𝗻𝗱 𝗝𝗲𝘁𝘀𝗼𝗻 𝗧𝗵𝗼𝗿 𝗶𝘀 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗮 𝗻𝗲𝘄 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗳𝗼𝗿 𝗘𝗱𝗴𝗲 𝗔𝗜 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀. In our latest article, we explore how this next generation of NVIDIA’s Edge AI stack simplifies vision AI development, boosts inference speed, and opens the door to new applications in retail analytics, smart cities, and industrial automation. With DeepStream 8, developers can build complex multi-camera and multimodal AI pipelines using simple configuration files - while Jetson Thor, powered by the Blackwell GPU architecture, delivers unprecedented performance per watt for demanding edge environments. At Namla, we focus on helping organizations orchestrate, secure, and scale these deployments across thousands of edge nodes using our cloud-native platform. 🔗 Read the full article: https://lnkd.in/efQihy4f #EdgeAI #JetsonThor #DeepStream8 #NVIDIA #Namla #AIInfrastructure #EdgeComputing #VisionAI #ScalingEdgeAI
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AI workloads relying on vector databases are hitting a new speed record. By offloading Milvus indexing to NVIDIA L40S GPUs using cuVS, and accelerating data movement with Cloudian S3 + RDMA, indexing time dropped from 2 hours to just 16 minutes. This breakthrough removes one of AI’s biggest data bottlenecks — making real-time RAG and vector search faster and more efficient than ever. 👉 Read how it’s done: https://bit.ly/3KYMRHD #AI #VectorSearch #RAG #GPUAcceleration #NVIDIA #Cloudian #Milvus #DataPerformance #AIStorage #AIinfrastructure #StorageforAI #AIindexing
Blog: Supercharge Vector Database Indexing with Cloudian and NVIDIA
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AI workloads relying on vector databases are hitting a new speed record. By offloading Milvus indexing to NVIDIA L40S GPUs using cuVS, and accelerating data movement with Cloudian S3 + RDMA, indexing time dropped from 2 hours to just 16 minutes. This breakthrough removes one of AI’s biggest data bottlenecks — making real-time RAG and vector search faster and more efficient than ever. 👉 Read how it’s done: https://bit.ly/3KYMRHD #AI #VectorSearch #RAG #GPUAcceleration #NVIDIA #Cloudian #Milvus #DataPerformance #AIStorage #AIinfrastructure #StorageforAI #AIindexing
Blog: Supercharge Vector Database Indexing with Cloudian and NVIDIA
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AI workloads relying on vector databases are hitting a new speed record. By offloading Milvus indexing to NVIDIA L40S GPUs using cuVS, and accelerating data movement with Cloudian S3 + RDMA, indexing time dropped from 2 hours to just 16 minutes. This breakthrough removes one of AI’s biggest data bottlenecks — making real-time RAG and vector search faster and more efficient than ever. 👉 Read how it’s done: https://bit.ly/3KYMRHD #AI #VectorSearch #RAG #GPUAcceleration #NVIDIA #Cloudian #Milvus #DataPerformance #AIStorage #AIinfrastructure #StorageforAI #AIindexing
Blog: Supercharge Vector Database Indexing with Cloudian and NVIDIA
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AI workloads relying on vector databases are hitting a new speed record. By offloading Milvus indexing to NVIDIA L40S GPUs using cuVS, and accelerating data movement with Cloudian S3 + RDMA, indexing time dropped from 2 hours to just 16 minutes. This breakthrough removes one of AI’s biggest data bottlenecks — making real-time RAG and vector search faster and more efficient than ever. 👉 Read how it’s done: https://bit.ly/3KYMRHD #AI #VectorSearch #RAG #GPUAcceleration #NVIDIA #Cloudian #Milvus #DataPerformance #AIStorage #AIinfrastructure #StorageforAI #AIindexing
Blog: Supercharge Vector Database Indexing with Cloudian and NVIDIA
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