We are honored to have met KAUST's President, Dr. Tony F Chan, and to work on revolutionary research together. At #SC23, King Abdullah University of Science and Technology (KAUST) and Cerebras Systems were finalists for the 2023 Gordon Bell Prize, the most prestigious award for
HARDWARE
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OS-Level Support: The Key to AI Innovation
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yeah, but I think the exciting stuff happens with os-level support
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Airgapped P2P AR Glasses for Decentralized Metaverse Infrastructure
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airgapped p2p AR glasses as the actual decentralized metaverse sounds interesting
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Vision Pro Local Multiplayer Will Drive Killer Immersive Applications
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vision pro will be cracked once it goes local multiplayer eg: walk around a shared project, watch tv together, play shared games in the same space
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Cerebras Sparsity Optimization for Foundation Model Training
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(10/n) Contact us to learn more about Cerebras and how sparsity can make training your next foundation model orders of magnitude more efficient. Shoutout to the amazing software, machine learning, and performance team members who’ve played an instrumental role in developing
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Cerebras CS-2 Accelerates Foundation Model Training Through Sparsity
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(5/n) Our library is hardware agnostic, but combined with the Cerebras CS-2's unique ability to accelerate unstructured #sparsity, it unleashes unparalleled efficiency in training foundation models.
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Sparsity Unlocks New ML Training Efficiency Dimension
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(3/n) Sparsity helps unlock a new dimension of efficiency beyond model architectures for training, enabling control of #computing performance in the ML practitioner's hand. Sparse models also achieve better scaling but are difficult to accelerate. Today's #deeplearning libraries
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AI-Powered Smart Glasses Integration with Real-Time Answers
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We're teaming up with @brilliantlabsAR to integrate our real-time answers into Frame, their AI-enhanced glasses. You can ask about what you see, instantly — all in your line of sight.
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LoRA vs Quantization: Optimizing AI Model Training Resources
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Yes! I could have done Lora as well but I opted for the quantized version to reduce resources. If I had more than 64gb of RAM I feel Lora would have been fine.
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MLX Framework for Apple Silicon Machine Learning
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Yeah I used MLX, it’s amazing! You find all what you need here. It’s designed for Apple Silicon https://
github.com/ml-explore/mlx
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