LLM-driven optimizer that auto-tunes CUDA kernels for multi-scenario GPUs

Where this came from

The pain

Automate expert GPU kernel tuning: use LLMs to generate, profile, and ensemble CUDA kernel variants across scenarios to deliver near-expert performance with minimal manual effort.

Who it is for

Mid-to-large enterprises and AI startups that run custom CUDA kernels or own model inference/training pipelines, have operational budgets for GPU spend, and maintain engineering teams (2-50 people) focused on performance. Technographically, customers run CUDA-capable fleets (NVIDIA A100/H100/RTX class), use containers/CI, and have telemetry tied to performance budgets. Behaviorally, they tolerate short PoCs and measure ROI in GPU-hours saved or throughput gains.

Source pains

• Model/generalization risk — LLM-generated kernels may be incorrect or suboptimal; robust validation and safety checks are required to avoid regressions.
• Vendor lock-in and portability — heavy coupling to a single GPU vendor limits addressable market; multi-vendor support increases complexity.
• Operational cost scaling — profiling many variants across scenarios can be expensive; customers will watch compute costs versus realized gains.