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 research.google
General and Scalable Parallelization for Neural Networks
| | jax-ml.github.io
How To Scale Your Model
3.4 parsecs away

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| | Training LLMs often feels like alchemy, but understanding and optimizing the performance of your models doesn't have to. This book aims to demystify the science of scaling language models: how TPUs (and GPUs) work and how they communicate with each other, how LLMs run on real hardware, and how to parallelize your models during training and inference so they run efficiently at massive scale. If you've ever wondered "how expensive should this LLM be to train" or "how much memory do I need to serve this model myself" or "what's an AllGather", we hope this will be useful to you.
| | www.primeintellect.ai
State-of-the-art in Decentralized Training
3.8 parsecs away

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| | hpc-ai.com
Enhanced MoE Parallelism, Open-source MoE Model Training Can Be 9 Times More Efficient
3.5 parsecs away

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| | We are delighted to announce a comprehensive upgrade to the ColossalAI-MoE module, which is specifically designed to enhance MoE models. This upgrade aims to assist users in training and deploying expert models efficiently and stably.
| | ssir.org
How AI-Powered Nonprofits Are Building the Next Generation of Education Tools
8.1 parsecs away

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| AI is being used in exciting ways to bridge educational divides, and AI-powered nonprofits are creating a roadmap for what the future of education may hold.