Artificial Intelligence engines are sucking up global water supplies - The Tanzania Times
<a href="https://news.google.com/rss/articles/CBMijgFBVV95cUxPalZRNFV2WUNxZXYzbE1QNjVmNFBpRW9UczZ6WU0zZHZSa2JCUm9XS1p2Vy12SjFKaTF4T3FBTXpyOFlpM3BrNUNrTFZxempxa1dKeTd2a0ZMakhGaXpPTjh2ZjFzX00wcjkxVE1vXzJUMS02Mm8tQ0hiVUhwZVJicHJURW1QVkhadm82cTRn?oc=5" target="_blank">Artificial Intelligence engines are sucking up global water supplies</a> <font color="#6f6f6f">The Tanzania Times</font>
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I wrote a fused MoE dispatch kernel in pure Triton that beats Megablocks on Mixtral and DeepSeek at inference batch sizes
Been working on custom Triton kernels for LLM inference for a while. My latest project: a fused MoE dispatch pipeline that handles the full forward pass in 5 kernel launches instead of 24+ in the naive approach. Results on Mixtral-8x7B (A100): Tokens vs PyTorch vs Megablocks 32 4.9x 131% 128 5.8x 124% 512 6.5x 89% At 32 and 128 tokens (where most inference serving actually happens), it's faster than Stanford's CUDA-optimized Megablocks. At 512+ Megablocks pulls ahead with its hand-tuned block-sparse matmul. The key trick is fusing the gate+up projection so both GEMMs share the same input tile from L2 cache, and the SiLU activation happens in registers without ever hitting global memory. Saves ~470MB of memory traffic per forward pass on Mixtral. Also tested on DeepSeek-V3 (256 experts) and
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