I think we’re a while out from that right? High end gpu feature so I don’t see game devs designing with dlss 5 as the default without a graceful fallback
HARDWARE
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Matching Inference Engines to GPUs and Model Architectures
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Each inference engine must implement the model architecture & its tool calling Getting a model to run correctly isn’t trivial, many parts are still inconsistent/broken Set the right baseline: match the GPUs, the inference engine for those GPUs & right inference engine for model
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TensorRT-LLM Optimized for DGX Spark: Inference Engines Matter
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Inference Engines have different optimizations for different hardware Most optimized for DGX Spark: TensorRT-LLM Inference Engines MATTER and they are NOT EQUAL (e.g. blogpost below) Opensource models ARE NOT just a memory size issue BTW
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Nvidia Robot OS, Navy Deals, Visual Memory Breakthroughs
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Top stories in robotics today: – Nvidia wants to be the OS of every robot
– Gecko Robotics lands $71M U.S. Navy deal
– Ex-Meta engineers give robots visual memory
– Robot dogs guard billion-dollar AI data centers
– Quick hits on other robotics news -
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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Atlas Evolution: From Walking to Warehouse Work
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Boston Dynamics’ Atlas Evolves: From Walking to Shelf Work, Tire Flips, and Even Folding Spot #Robots
— Ronald van Loon (@Ronald_vanLoon) 19 mars 2026
by @lukas_m_ziegler#Robotics #ArtificialIntelligence #Innovation #MI #ML #Tech pic.twitter.com/myWzl1GFS0Boston Dynamics' Atlas Evolves: From Walking to Shelf Work, Tire Flips, and Even Folding Spot #Robots
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@lukas_m_ziegler #Robotics #ArtificialIntelligence #Innovation #MI #ML #Tech -
Building AI-Powered Dog-Shaped Robots: Complete Guide
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Learn How to Build an #AI-Powered Dog-Shaped #Robot from Scratch
— Ronald van Loon (@Ronald_vanLoon) 19 mars 2026
via @ZappyZappy7
#EmergingTech #Robotics #Innovation #Technology pic.twitter.com/q2SKRYZu13Learn How to Build an #AI-Powered Dog-Shaped #Robot from Scratch
via @ZappyZappy7 #EmergingTech #Robotics #Innovation #Technology -
Northeastern University Unveils Innovative Hybrid Wheel-Leg Robot
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Northeastern University Unveils Hybrid Wheel-Leg #Robot That Rolls Fast and Walks Over Rough Terrain
— Ronald van Loon (@Ronald_vanLoon) 19 mars 2026
by @tweetciiiim
#Robotics #ArtificialIntelligence #Innovation #Technology pic.twitter.com/YUTlcbW5soNortheastern University Unveils Hybrid Wheel-Leg #Robot That Rolls Fast and Walks Over Rough Terrain
by @tweetciiiim #Robotics #ArtificialIntelligence #Innovation #Technology