Metal

AI Kernel Generation: What's working, what's not, what's next – Natalie Serrino, Gimlet Labs

Dec 17, 2025 · 19:15

Natalie Serrino, cofounder of Gimlet Labs, presents how AI-generated kernels can automatically speed up custom PyTorch code by up to 24% on Apple M4 hardware using the Metal framework, with a 40% speedup from kernel fusion. The agentic system iterates through compilation, execution, correctness, and optimization, but faces challenges like validation of floating-point results and reliable benchmarking. Successes include rewriting average pool 1D as a convolution for 80% improvement, while failures occur on heavily optimized ops like matrix multiply. A real-world audio encoder model saw 70% faster inference on RTX 6000 Blackwell via six custom fused kernels. Serrino emphasizes that AI is best for rapidly searching optimizations and porting code to new hardware, not for surpassing human experts on novel algorithms.

Luminal - Search-Based Deep Learning Compilers - Joe Fioti

Jun 3, 2025 · 24:35

Joe Fioti presents Luminal, a search-based deep learning compiler that simplifies ML libraries to 12 primitive operations and uses search to automatically discover optimized kernels like flash attention. By representing models as directed acyclic graphs of these simple ops, Luminal keeps its codebase under 5,000 lines yet can run all major models. Its compiler applies 20-25 rewrite rules to search through equivalent GPU kernels, profiling to find the fastest—automatically rediscovering flash attention, an algorithm that took five years for the industry to develop. Data movement accounts for 99% of runtime, so kernel fusion merges many ops into one, dramatically speeding execution. An external auto-grad crate adds training support without altering the core. Future plans include supporting AMD, TPUs, and a serverless cloud that exports optimized graphs for inference.