yea that and other SIMD / parallelization unoptimizations
COMPUTING
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Cerebras Wins Gold for Most Innovative AI Tech Company
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We’re proud to announce that Cerebras has been recognized as a Gold winner in @TheStevieAwards 22nd Annual American Business Awards® for Most Innovative Tech Company of the Year! “Cerebras Systems' groundbreaking achievements in AI technology, demonstrated by the innovative
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speedAI: Revolutionary AI Hardware Acceleration Solution Launch
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The future of #AI at our fingertips. Literally. Engineered for unparalleled performance, speedAI® is the ultimate solution for your AI acceleration needs. Interested in getting your hands on one of these? Learn how at http://
untether.ai/products. -
Inference Compute Shortage: Foundation Models Need More Resources
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Much has been said about many companies’ desire for more compute (as well as data) to train larger foundation models. I think it’s under-appreciated that we have nowhere near enough compute available for inference on foundation models as well. Years ago, when I was leading teams
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NVIDIA Aerial Omniverse enables 5G 6G R&D digital twin simulation
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Learn how R&D on #5G and #6G and optimized network planning is enabled through NVIDIA Aerial™ Omniverse™ Digital Twin—a next-generation, system-level simulation platform now available in the NVIDIA 6G Research Cloud platform. Explore the platform. https://
nvda.ws/3vYcEc1 -
Software Systems Design: Beyond Explicit Function Engineering
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I think many people are used to thinking of software systems as something where all the functions are explicitly designed.
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Red Hat Summit 2026: AI Insights with Partners and Customers
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We are happy to be back at this year's Red Hat Summit, May 6–9. https://
redhat.com/en/summit We'll be alongside thousands of Red Hat partners, customers, and peers to learn and share the latest insights into AI with keynotes, breakout sessions, demos, and more. #RHSummit -
NVIDIA Delivers First DGX H200 to OpenAI for AI Advancement
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First @NVIDIA DGX H200 in the world, hand-delivered to OpenAI and dedicated by Jensen "to advance AI, computing, and humanity":
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Enterprise AI at Scale: GPU Infrastructure Challenges
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Transforming Enterprise AI at Scale "Scale sets a speed limit; if you don't have scale, you can't train some of these massive models." In a recent interview with Sequoia, Andrej Karpathy mentions this and highlights the difficulty with instrumenting large GPU clusters that
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Fast Generation in Large Models with Sparse Parameter Activation
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Not sure how it will play out, but it means that generations will be very fast for such a big model, as only a small portion of params will be active at a time.