I developed a custom GPU kernel in Triton to accelerate training with a sparse feed-forward network (FFN). With K=256, activating 256 of 4,096 FFN neurons per token (6. 25%), the sparse model achieved 36% higher training throughput than its dense counterpart.

Source: [Hacker News](https://huggingface.co/JakCwi/eugen-sparse)

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