Latest blog from @UCIexpress highlights our role in driving #AI innovation beyond data centers to the network edge with our at-memory architecture for neural network inference. Read more about our part in shaping the future of AI through #UCIe's universal interconnect framework.
HARDWARE
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Quantum vs Classical Computers: Power, Reliability Trade-offs
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Quantum computers use qubits for complex tasks, with power growing exponentially per qubit, but they face high error rates and require cold temperatures. Meanwhile, classical computers are more reliable and work at room temperature, suiting daily tasks. Microblog @antgrasso
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Hybrid AI Workflow: Scaling Performance with RTX Cloud
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Boost computing performance with a hybrid AI workflow. Discover how combining local RTX capabilities with the NVIDIA-powered cloud can scale performance of your most demanding AI workloads. Learn more in the latest #AIDecoded blog https://
nvda.ws/3VmGOj6 -
Intel 5th Gen Xeon Processors: Sub-100ms Problem Solving
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New processors anticipate and solve problems in less than 100ms. Faster than the blink of your eye (up to 400ms)!@Intel Corporation unveiled the 5th Gen Xeon Processors. 🎉 Fast, powerful, and eco-friendly! #5thGenIntelXeon #IntelAmbassador pic.twitter.com/ErNUadXxhD
— Pascal Bornet (@pascal_bornet) 29 mai 2024New processors anticipate and solve problems in less than 100ms. Faster than the blink of your eye (up to 400ms)! @Intel Corporation unveiled the 5th Gen Xeon Processors. Fast, powerful, and eco-friendly! #5thGenIntelXeon #IntelAmbassador
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Stanford Humanoid Robot Explores Deep Ocean With 3D Vision and Haptics
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This is a humanoid #robot by Stanford researchers, designed to explore depths unreachable by humans. With 3D vision and a haptic interface, it lets operators see and feel underwater environments! #AI #IoT #5G @IntEngineering
— Harold Sinnott #MWC26 (@HaroldSinnott) 29 mai 2024
pic.twitter.com/VO9Z07YaixThis is a humanoid #robot by Stanford researchers, designed to explore depths unreachable by humans. With 3D vision and a haptic interface, it lets operators see and feel underwater environments! #AI #IoT #5G @IntEngineering
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GPU Supply Chain Bottlenecks: Chips, Power, Interconnection Challenges
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6/ On compute—we have seen the astronomical investment into GPUs with NVIDIA's revenue growth. Staggering. Now the bottleneck is going to be—
– how many more chips can we make
– where will they go
– where will the power come from
– how tightly can we interconnect them -
H100 and FP8 optimizations for performance improvement
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Nice! H100 is a great "free win" to bring this down.
Turning on fp8 for GEMMs would be the other source of really solid improvement, imminently -
GPU acceleration and XLA compilation limitations in framework integration
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But then you would not be able to run it on GPU, nor compile to XLA for a ~5x speedup, nor use it inside a TensorFlow codebase or a PyTorch codebase.
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Free Meetup: Building LLM Serving Platforms at Scale with NVIDIA
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Bay Area Friends: Join us tomorrow at Orchestrating #GenAI Apps Meetup with NVIDIA and NetApp. We'll be discussing what it takes to build an #LLM serving platform at scale. Best of all it's free to attend, save your spot:
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Single Node Training vs Large-Scale Multi-GPU Distributed Runs
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But those were also much much bigger runs, so it's a lot more impressive. This was on a single node so you don't need to deal with any cross-node interconnect. It starts to get a lot more fun when you have to keep track of O(10,000) GPUs all at once. For a very specific