# EUROPA explains how it trained a 1T-parameter MoE on JUPITER with PyTorch

> Original title: Training a 1T-Parameter MoE on EU public infrastructure with PyTorch and TorchTitan

- Source: PyTorch Blog
- Published: 2026-11-12T12:00:19.000Z
- Bad Influence: https://badinfluence.news/items/cqc5gq96hi7q8w5p9fa6cm00i
- Original: https://pytorch.org/blog/training-a-1t-parameter-moe-on-eu-public-infrastructure-with-pytorch-and-torchtitan/

## Summary

The EUROPA consortium says it raised training throughput on the JUPITER supercomputer from 90 to about 1,300 tokens/s per GPU across 384 GH200 GPUs. The post describes the changes behind the gain, including FlexShard placement for the Muon optimizer, activation-checkpoint offloading to the Grace CPU, and running FSDP's all-gather on the compute stream.

## Why it matters

The post gives specific throughput changes and optimization steps for training a 1T-parameter MoE on shared public GPUs, useful for anyone facing similar interconnect or memory limits.