Not OP but I think this could be an instance of leaky abstraction at work. Most of the time you hand-write an accelerator kernel hoping to optimize for runtime performance. If the abstraction/compiler does not fully insulate you from micro-architectural details affecting performance in non-trivial ways (e.g. memory bank conflict as mentioned in the article) then you end up still having per-vendor implementations, or compile-time if-else blocks all over the place. This is less than ideal, but still arguably better than working with separate vendor APIs, or worse, completely separate toolchains.

The blog post is about using an NVIDIA-specific tensor core API that they have built to get good performance.

Modular has been pushing the notion that they are building technology that allows writing HW-vendor neutral solutions so that users can break free of NVIDIA's hold on high performance kernels.

From their own writing:

> We want a unified, programmable system (one small binary!) that can scale across architectures from multiple vendors—while providing industry-leading performance on the most widely used GPUs (and CPUs).