The reason FlashAttention matters is that modern transformers are memory-bound — FlashAttention reduces memory traffic, keeping data local and maximizing effective GPU utilization.

GPUs are fast because they run massive numbers of threads in parallel, and their scheduler hides memory latency by keeping warps busy so compute units stay utilized.

I’m focusing on GPU inference performance (FlashAttention / CUDA / Triton). The next breakthrough isn’t bigger models — it’s removing memory bottlenecks and moving data smarter.

Deep learning doesn’t rely on hand-coded rules. Models learn latent representations — the real source of understanding + generalization.