# NVIDIA GB300 NVL72

Meta converted a conventional data center in New Albany, Ohio, into Prometheus, a gigawatt-scale supercluster whose first phase is complete and was used to train the Muse family of foundation models. Because one failing host can slow an entire large training job, Meta focused on system stability and reached more than 85% goodput on Muse Wonder pre-training runs and more than 90% on reinforcement learning runs. The cluster now spans hundreds of thousands of GPUs, with a target of roughly one million at full scale.

Meta converted a conventional data center in New Albany, Ohio, into Prometheus, a gigawatt-scale supercluster whose first phase is complete and was used to train the Muse family of foundation models. Because one failing host can slow an entire large training job, Meta focused on system stability and reached more than 85% goodput on Muse Wonder pre-training runs and more than 90% on reinforcement learning runs. The cluster now spans hundreds of thousands of GPUs, with a target of roughly one million at full scale.

Meta released Muse Spark Open, an open mixture-of-experts model for agentic coding with 816B total parameters, 42B active per token, and a 1M token context window that holds an entire project including complex dependencies and legacy code. General coding models are optimized for single-shot completions such as generating a function or a unit test, where context is small and the answer immediate, while multifile refactors, navigation across thousands of files, recovery from failing tests, and subagent coordination require sustained multistep work. Trained on NVIDIA GB300 NVL72, the model supports tool use, output evaluation, and iterative refinement, using goal conditioning to hold direction and context compaction to sustain progress across long sessions.

Meta released Muse Spark Open, an open mixture-of-experts model for agentic coding with 816B total parameters, 42B active per token, and a 1M token context window that holds an entire project including complex dependencies and legacy code. General coding models are optimized for single-shot completions such as generating a function or a unit test, where context is small and the answer immediate, while multifile refactors, navigation across thousands of files, recovery from failing tests, and subagent coordination require sustained multistep work. Trained on NVIDIA GB300 NVL72, the model supports tool use, output evaluation, and iterative refinement, using goal conditioning to hold direction and context compaction to sustain progress across long sessions.