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Working notes on production machine-learning systems — diffusion inference, GPU infrastructure, and the distance between a published method and what actually ships.

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  • Owning the Inference Path: Handwritten fp8 Kernels, 2.5x Cheaper GPU Inference

    An image-generation product lives or dies by its GPU bill. This is how per-image compute on Narcis fell 2.5x — no new model, no extra hardware: strip the library stack, use torch.compile as a teacher, and replace it with seven handwritten fp8 Triton kernels. 1.75s to 0.687s per step on a 6.15B diffusion transformer, serving production on NVIDIA L4 today.
  • Narcis.ai: Production ML Systems Engineering

    Production systems built through deep understanding and minimal complexity — infrastructure foundations, a two-stage diffusion pipeline, and what it takes to run face-identity generation live.

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