Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUD

<!-- SC_OFF --><div class="md"><p>The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

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arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX2SQ594" rel="noopener noreferrer">System Design Pocket Guide: Interviews</a> </li> <li> <strong>Also by

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<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>I'm an independent researcher sharing Monodratic, a sparse causal-attention architecture with learned product-hash