From One Node to Distributed Training and Inference. How the PyTo...
summary
Claims that single‑GPU or single‑node inference is obsolete as open models exceed 500 billion parameters — DeepSeek 671B and MyMovie 2 Pro 1T — and that these models run 24/7 in production while compute grows 5x/year. Describes concrete stack components: Monarch, Slurm, Kubernetes for orchestration; TorchTitan, TorchFT, ROCm and Flight DSL, OpenAI Triton, Helion for scaling, fault tolerance and vendor‑agnostic kernels. Argues the ecosystem is already enabling distributed training and inference at scale and that AMD has open‑sourced key pieces.
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