Reproducing & Validating Distributed Muon (MoonshotAI) — Performance & Communication Results

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Hi everyone!

I just published a full technical report where I reproduced MoonshotAI’s Distributed Muon optimizer, validated their communication efficiency claims, and profiled DP/TP configurations on a 4-GPU cluster.

The post includes:
• Full Muon DP=2/TP=2 and Adam profiling
• Perfetto traces (communication patterns)
• Memory analysis
• Two bug fixes to the open-source PoC
• Async-op experiments (and why naive overlap slows things down)

Key results:
• 0.57× communication compared to AdamW
• 1.1% optimizer overhead
• 50% less state memory

Write-up here

:backhand_index_pointing_right: Reproducing and Validating Distributed Muon :turtle::sparkles:: A Practical Verification of Communication Efficiency Claims

I’m preparing a cleaned-up repo next. If you are experimenting with Muon, distributed optimizers, or multi-node scaling, happy to collaborate or cross-validate results.

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The optimizer overhead number made me grin because I have spent way too many nights fighting with bloated state updates. Seeing Muon behave this lean gives me a strange feeling of hope and mild jealousy at the same time.

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Haha, the ‘bloat’ is the enemy of us all! :handshake: While I haven’t battled 36B models personally (yet!), seeing the math play out was definitely a ‘hopeful’ moment.

I know whether to use Muon is tricky for fine-tuning (especially if you use LoRA), but if you ever want to mess around with the distributed setup or see the raw traces, I just pushed the full reproducibility suite

:backhand_index_pointing_right: : bird-of-paradise/muon-distributed-reproducibility · Datasets at Hugging Face .

Good luck with that 36B model—that sounds like a beast to wrangle!

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I also published the report on Hugging Face Article

:backhand_index_pointing_right: Reproducing and Validating Distributed Muon 🐢✨: A Practical Verification of Communication Efficiency Claims

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