Taming the AI Hardware Cambrian Explosion — Abdul Dakkak, Modular

This talk introduces Modular's approach to addressing the AI hardware Cambrian explosion by rebuilding the software stack from the ground up. It presents Mojo, a Pythonic systems language designed for high performance across CPUs, GPUs, and accelerators, aiming to match CUDA's performance with open-sourced kernels that claim to outperform vendor libraries and FlashAttention 4. The talk also introduces MAX, an inference framework that rethinks PyTorch-era assumptions to support thousands of model architectures and run across heterogeneous hardware (multi-GPU, multi-node, mixed hardware). Modular Cloud is presented as a tunable glass box to tie these components together. Benchmarks are shared comparing performance against vLLM and torch.compile on various hardware, demonstrating sub-second image generation and performance on models like Kimi K2.5, FLUX.2, AMD MI355, and Gemma 4.