Rakesh said something that stuck with me: “AI infrastructure is about building a massive computer with huge amounts of network bandwidth.” Read that again. AI isn’t just compute. It’s: → Extreme east-west traffic
→ Massive bandwidth demands
→ Tight coupling between
COMPUTING
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AI Infrastructure: Beyond Compute – Bandwidth and Network Coupling
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Enterprise AI Failures: Infrastructure, Not Models, Is the Problem
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Most enterprise AI initiatives don’t fail because of bad models.
— Ronald van Loon (@Ronald_vanLoon) 19 mars 2026
They fail because they’re running on infrastructure that was never designed for AI.
A traditional data center is not the same thing as AI infrastructure.
This came up directly in my conversation with… pic.twitter.com/EeakEubh5hMost enterprise AI initiatives don’t fail because of bad models. They fail because they’re running on infrastructure that was never designed for AI. A traditional data center is not the same thing as AI infrastructure. This came up directly in my conversation with
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Time-Series Databases for Industrial Energy Anomaly Detection
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Sponsored by InfluxData. Energy anomaly detection at the millisecond level requires a database architecture built specifically for time-series data. High-frequency sensor ingestion at industrial scale generates volumes that general-purpose databases cannot handle reliably at
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IBM NVIDIA Accelerate Data Processing with AI cuDF Technology
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.@IBM and NVIDIA are reinventing data processing for the era of AI.
— NVIDIA AI (@NVIDIAAI) 19 mars 2026
By accelerating IBM with NVIDIA cuDF, @Nestle is seeing transformative results:
✅5x faster data workloads
✅83% lower costs
In a massive logistics network, "faster" means responding in minutes.
The next… pic.twitter.com/SDLTnehzYA.
@IBM and NVIDIA are reinventing data processing for the era of AI. By accelerating IBM with NVIDIA cuDF, @Nestle is seeing transformative results: 5x faster data workloads
83% lower costs In a massive logistics network, "faster" means responding in minutes. The next -
Hack #05: AI in Space Hackathon Registration Now Open
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AI is beginning to move beyond the clouds… Registration is open for Hack #05: AI in Space, in collaboration with @DPhiSpace. A hackathon exploring what becomes possible when AI operates closer to satellites, orbital systems, and space-based data. For developers, researchers, and builders interested in the future of AI in space. Register → luma.com/n9cw58h0 Learn more → hackathons.liquid.ai 🚀 Join the conversation → discord.com/channels/1385439…
→ View original post on X — @maximelabonne, 2026-03-19 15:04 UTC
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DGX Spark vs RTX PRO 6000 Memory Bandwidth: Why Tool Choice Matters
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DGX Spark uses unified memory > 273 GB/s RTX PRO 6000 delivers > 1.8 TB/s (1792 GB/s) If someone told you they’re comparable, they’re wrong And this is exactly why llama.cpp isn’t the right tool here Try vLLM or SGLang on a GPU and you’ll see very different results
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NVIDIA GTC Booth: Dell and Nebiusai Partnership Showcase
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TGI(almost)F. It's day four of @NVIDIAGTC – time really does fly. We'll be at the booth starting at 11am – come meet the team and learn more about our @Dell and @nebiusai partnerships!
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Building AI Superpower Requires Complete Stack Infrastructure
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You don’t become an AI superpower by writing clever code.
— Nina Schick (@NinaDSchick) 19 mars 2026
You need the entire stack — from industrial base to compute infrastructure, to models and real-world deployment. Software alone won’t cut it.
At the foundation: energy, materials, manufacturing. Then comes the engine —… pic.twitter.com/62SaoVh65uYou don’t become an AI superpower by writing clever code. You need the entire stack — from industrial base to compute infrastructure, to models and real-world deployment. Software alone won’t cut it. At the foundation: energy, materials, manufacturing. Then comes the engine —
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TDK SensEI Automates Industrial Problem Detection and Resolution
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Detecting a problem is step one. Knowing how to fix it without waiting for a technician to research it is step two. Bob Roth at AWS re: Invent explained how TDK SensEI automates both. Partner Content with TDK SensEI. #tdk_iiot pic.twitter.com/3IfqLkZ7Qx
— Lucian Fogoros (@fogoros) 19 mars 2026Detecting a problem is step one. Knowing how to fix it without waiting for a technician to research it is step two. Bob Roth at AWS re: Invent explained how TDK SensEI automates both. Partner Content with TDK SensEI. #tdk_iiot
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ILM Separation Maintains Speed Without Core Pressure
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True, separating ILM helps maintain speed without adding pressure on the core, which is key for consistent delivery.