Optimizing CUDA kernels is often complex and requires expert knowledge. So I built a tool that simplifies this process by profiling your code on serverless GPUs (matched to your GPU architecture), identifying bottlenecks in your kernel code, and applying SOTA optimization techniques to deliver up to 20x faster performance, all without any manual tuning.
DeepSeek’s R1 showed how optimizing kernels can significantly reduce costs while improving performance. So, I took this a step further by automating the process and make it effortless to maximize GPU efficiency and eliminate unnecessary overhead.
If you're working in AI research, deep learning, scientific computing, or any high-performance application, I'd love to hear your feedback.
Try it out here: https://www.rightnowai.co/
I’m sorry for not providing a free trial, but because profiling on GPUs is costly, it prevents me from offering that feature at the moment.
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