The Agentic Control Plane: Why Every AI Platform Will Need This Layer And Most Don't Have It YetJun 8, 2026·8 min read
Operators for the Inference Era: Simplifying LLM Serving on KubernetesTL;DR: The AI industry has moved from training-heavy workloads to inference-heavy production deployments, making LLM serving infrastructure the new bottleneck. Kubernetes alone is not enough: GPU sJun 15, 2026·9 min read
Confidential AI Meets Sovereign AI: Building Trust into India's AI Stack TL;DR Trust is the next infrastructure layer. As Indian enterprises scale AI, the biggest bottleneck is no longer compute, it's confidence: in where data lives, who can access models, and how decisioApr 20, 2026·10 min read
Project Orion: Taking Orbital AI Infrastructure Beyond EarthTL;DR AI is no longer limited by models. It is limited by delivery speed and infrastructure reach Traditional datacenters struggle with latency, accessibility, and uneven global distribution ProjecApr 14, 2026·6 min read
Agentic AI at Enterprise Scale: From Scripts to Autonomous SystemsTL;DR Agentic AI is not an upgrade to automation, it is a structural shift from rule-bound scripts to goal-directed, self-orchestrating systems. Most enterprises are stuck in "RAG-plus-workflow" terApr 6, 2026·8 min read
Inside GB300 Architecture: Memory, Bandwidth & AI Performance Explained TL;DR GB300 architecture is built to remove the biggest bottleneck in AI workloads: memory bandwidth and data movement The combination of Grace CPU + Blackwell GPU delivers tighter CPU-GPU integratiMar 30, 2026·7 min read
GB200 NVL72 GPU Demystified: Performance, Pricing & Deployment TipsTL;DR – NVIDIA GB200 NVL72 GPU Rack-scale AI supercluster with 72 Blackwell GPUs. Unified compute system via high-speed NVLink. Optimized for LLM training, generative AI, multimodal AI, and real-tiMar 5, 2026·9 min read
Leveraging Tensor Cores and Mixed Precision for Cost-Effective LLM Training at ScaleTL;DR Tensor Cores for LLM training combined with mixed precision training for LLMs can reduce training costs by 30 to 50 percent while improving throughput. Moving from FP32 to FP16 or BF16 is no lFeb 24, 2026·6 min read
