Ion Stoica traces Ray's evolution into the distributed compute layer for AI, plans Rust rewrite
PyTorch · x · 2026-10-21
At PyTorchCon 2026, Ion Stoica (UC Berkeley/Anyscale) traced Ray's journey from reinforcement learning origins to the distributed compute layer behind today's RL frameworks.
- Covers growing complexity across data processing, training, serving, and post-training.
- Highlights: topology-aware scheduling for TPU pods and NVIDIA Blackwell GPUs, GPU-to-GPU transfers via NIXL, and native TorchFT integration.
- Roadmap: rewriting major Ray components in Rust, tighter Kubernetes integration, and working with hardware providers on accelerator support.
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