Modular: Taming the AI Hardware Cambrian Explosion#

Speakers: Abdul Dakkak (Modular)

• Why: Rewriting kernels for every accelerator vendor is not sustainable, and performance portability across NVIDIA, AMD, Apple Silicon, and CPU is a real strategic question for heterogeneous fleets. But this is a platform session with vendor-reported numbers — adjacent, not core.
• What: Modular's pitch: Mojo kernels, the MAX compiler and runtime, and Modular Cloud together optimizing GenAI workloads from model graph to hardware execution, with claimed 10x lower latency on FLUX2 and 5.5x higher throughput on Kimi K2.5 over the state of the art (from abstract only). These are vendor-reported benchmarks — treat as claims to verify on your own workloads, not results.
• How: The genuinely transferable content is the bottleneck taxonomy for production inference — memory movement, batching, KV-cache layout, quantization, scheduling, kernel specialization — and the discussion of where performance-portability abstractions leak.
• Where applicable: Heterogeneous accelerator deployments; a commercial-platform session, so weigh the analysis over the multiples.