PTX
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You literally have never used deep learning frameworks.
All production systems use runtime kernel generation. Nobody’s is writing CUDA beforehand, because the best performing kernel is model and hardware dependent. Static kernels are outdated and nobody’s using this.
All modern production ready frameworks for training and inference are using PTX, nobody’s is writing CUDA kernels beforehand.

You are confidently incorrect about the exact frameworks you just named:
1. FlashAttention and PagedAttention (vLLM) are literally written in CUDA C++. Check their GitHub repos; they are packed with .cu files and built heavily on NVIDIA’s own CUTLASS library.
2. Triton relies entirely on the CUDA ecosystem. PyTorch uses Triton so developers can write Python instead of C++. Triton compiles that Python to LLVM IR, which then requires NVIDIA’s NVVM compiler (part of the CUDA Toolkit) to output the PTX.
3. CUDA allows exact control. Just like C++ allows inline assembly, CUDA C++ allows inline PTX (asm()) whenever a developer wants to bypass the compiler.
The community inventing new algorithms on top of CUDA doesn't mean CUDA failed LMAO. It means CUDA is the foundation everything is built on.

PTX is literally NVIDIA’s lowlevel CUDA assembly language. Saying CUDA is useless because frameworks target PTX is like saying C++ is useless because computers run machine code.
Deep learning frameworks use the CUDA Driver API and CUDA Runtime Compilation (NVRTC) to generate and execute that exact PTX. You are literally describing how the CUDA ecosystem works to argue that nobody uses it.

No, it’s not.
Nobody’s using CUDA.
All deep learning frameworks for training and inference use PTX and runtime kernel generation.
CUDA is useless

You literally have never used deep learning frameworks.
All production systems use runtime kernel generation. Nobody’s is writing CUDA beforehand, because the best performing kernel is model and hardware dependent. Static kernels are outdated and nobody’s using this.
All modern production ready frameworks for training and inference are using PTX, nobody’s is writing CUDA kernels beforehand.

You are confidently incorrect about the exact frameworks you just named:
1. FlashAttention and PagedAttention (vLLM) are literally written in CUDA C++. Check their GitHub repos; they are packed with .cu files and built heavily on NVIDIA’s own CUTLASS library.
2. Triton relies entirely on the CUDA ecosystem. PyTorch uses Triton so developers can write Python instead of C++. Triton compiles that Python to LLVM IR, which then requires NVIDIA’s NVVM compiler (part of the CUDA Toolkit) to output the PTX.
3. CUDA allows exact control. Just like C++ allows inline assembly, CUDA C++ allows inline PTX (asm()) whenever a developer wants to bypass the compiler.
The community inventing new algorithms on top of CUDA doesn't mean CUDA failed LMAO. It means CUDA is the foundation everything is built on.

PTX is literally NVIDIA’s lowlevel CUDA assembly language. Saying CUDA is useless because frameworks target PTX is like saying C++ is useless because computers run machine code.
Deep learning frameworks use the CUDA Driver API and CUDA Runtime Compilation (NVRTC) to generate and execute that exact PTX. You are literally describing how the CUDA ecosystem works to argue that nobody uses it.

No, it’s not.
Nobody’s using CUDA.
All deep learning frameworks for training and inference use PTX and runtime kernel generation.
CUDA is useless