Automated LLM-driven multi-scenario CUDA kernel optimizer mapping profiling to expert transforms

Where this came from

The pain

Use LLMs + profiling to automatically optimize CUDA kernels across input scenarios, producing expert-level transformations, tuned builds, and measurable GPU performance gains — saving engineers weeks of manual tuning.

Who it is for

Mid-market to enterprise engineering teams building GPU-accelerated software: ML infrastructure teams, HPC groups, game engine teams, real-time simulation and finance quant firms. Firmographics: 50-5,000 employees, $50M+ revenue for primary enterprise customers. Technographics: use CUDA, PyTorch/TF with custom kernels, CI/CD pipelines, use Nsight or similar profilers. Behavior: performance-conscious, have observable telemetry, and a track record of paying for tools that reduce infra cost or unlock product capabilities.

Source pains

• Model accuracy risk — LLM-suggested transforms might compile but regress performance or correctness; mitigation is to require regression-tested benchmarks and staged rollout.
• Vendor changes — GPU driver or ISA changes could break transforms; mitigation is continuous integration with vendor test matrices and rapid model fine-tuning.
• Data moat fragility — competitors can collect profiling data too; mitigation is early partnership deals and proprietary instrumentation across customers.