Somalia taps BPC to launch first nationwide instant payment system - FinTech Futures
<a href="https://news.google.com/rss/articles/CBMiwgFBVV95cUxQcDFZUU4wVVU3UnJENXk3TmR0aVpndURBZEhzdXY5M0JOeXRiOGZTRkJ0YVNDYUhaeUJBeTR6bjZtUWhvejJrMmVwbTAzNnY5V0YxcDJsT0taRkdFTXpsWlVNWjJHVDh6WmhVRWlNeGVuT1UzZVlydzdkWFQ2aGlXWFlhQ1o0MzBza2xEdU1aRks5NDRldGdfWHQ2LVlUUWJfVVFRRHJFb3gxNVF2ZlJ6ZWpSVGU3ZGtKN1UtYUI4d0swdw?oc=5" target="_blank">Somalia taps BPC to launch first nationwide instant payment system</a> <font color="#6f6f6f">FinTech Futures</font>
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launch![[P] Fused MoE Dispatch in Pure Triton: Beating CUDA-Optimized Megablocks at Inference Batch Sizes](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-matrix-rain-CvjLrWJiXfamUnvj5xT9J9.webp)
[P] Fused MoE Dispatch in Pure Triton: Beating CUDA-Optimized Megablocks at Inference Batch Sizes
I built a fused MoE dispatch kernel in pure Triton that handles the full forward pass for Mixture-of-Experts models. No CUDA, no vendor-specific code. On Mixtral-8x7B (A100), it beats Stanford's Megablocks at inference-relevant batch sizes (131% at 32 tokens, 124% at 128 tokens). At larger batches Megablocks' hand-tuned CUDA pulls ahead as expected. Two main contributions: Fused gate+up projection - both GEMMs share the same input tile load, SiLU computed in registers. Eliminates ~470MB of intermediate buffers per forward pass (35% memory traffic reduction). Block-scheduled grouped GEMM - precomputed block_id to (expert_id, offset) mapping handles variable-sized expert batches in a single kernel launch without padding. Tested across Mixtral-8x7B, DeepSeek-V3 (256 experts), and Qwen2-MoE. F
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