Uganda, UN Health Industry Foundation Launch AI Partnership to Boost Youth Skills, Smart Agriculture - Nilepost News
Uganda, UN Health Industry Foundation Launch AI Partnership to Boost Youth Skills, Smart Agriculture Nilepost News
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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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I built a faster alternative to cp and rsync — here's how it works
I'm a systems engineer. I spend a lot of time copying files — backups to USB drives, transfers to NAS boxes, moving data between servers over SSH. And I kept running into the same frustrations: cp -r is painfully slow on HDDs when you have tens of thousands of small files rsync is powerful but complex, and still slow for bulk copies scp and SFTP top out at 1-2 MB/s on transfers that should be much faster No tool tells you upfront if the destination even has enough space So I built fast-copy — a Python CLI that copies files at maximum sequential disk speed. The core idea When you run cp -r , files are read in directory order — which is essentially random on disk. Every file seek on an HDD costs 5-10ms. Multiply that by 60,000 files and you're spending minutes just on head movement. fast-cop
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