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venam.net | ||
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www.cherryservers.com
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| | | | | This guide will give you a comprehensive overview of GPU architecture, specifically the Nvidia GPU architecture and its evolution. | |
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www.rastergrid.com
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www.jmeiners.com
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| | | | | [AI summary] The provided text outlines the development of an LC-3 virtual machine (VM) in C, including the implementation of various instructions, memory operations, and input/output handling. It also discusses an advanced C++ approach using templates and bitwise flags to reduce code duplication and improve efficiency. The text covers topics like instruction decoding, memory addressing, flag handling, and platform-specific input buffering. Additionally, it references contributions from the community and mentions GitHub tags for organizing implementations in different languages. | |
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jax-ml.github.io
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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. | ||
