add-jit-kernel

To add a custom CUDA JIT kernel to sglang, you can use this Skill to implement the kernel using project abstractions, generate Python wrappers for invocation, and validate correctness with provided testing utilities.

Creating Python wrappers for CUDA JIT kernels is automated by this Skill, which generates the necessary Python bindings to enable easy invocation of your custom kernels within the sglang framework.

Benchmarking and testing custom CUDA kernels for AI workloads is facilitated by this Skill through writing consistent tests and benchmarks to ensure both correctness and performance before deployment.

Yes, sglang supports integrating lightweight custom CUDA functions, allowing you to implement element-wise operations like tensor scaling with validation tests through established conventions and abstracted utilities.

The best way to streamline deployment of custom CUDA kernels is using this Skill, which reduces development complexity by providing templates, validation, and benchmarking for safe integration into the sglang framework.