My Muon Replication Journey — From Distributed Optimizers to a No-BS Training Glossary 🧩

Show and Tell
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Hi everyone :waving_hand:

I’ve been diving deep into the “Muon is Scalable for LLM Training” paper by Moonshot — trying to decode and replicate as much as I can to understand what it really takes to make Muon production-ready.

This all started when I uploaded a Muon tutorial on the Hub, and it unexpectedly got a lot of downloads. That reaction made me realize how many of us are curious about powerful optimizers like Muon. But the real question is how it actually scale in practice — beyond the tutorial.

So, I decided to really get to the bottom of this paper — and share what I uncover in a multi-part Medium series:

:turtle: Part 1 — “The Turtle Speed Breakthrough: Decoding Distributed Optimizers from FSDP to Muon’s Secret Sauce”

:backhand_index_pointing_right: Link here

This post focuses on distributed optimizers — how memory sharding in ZeRO and FSDP works under the hood, and what makes Muon’s optimizer unique.

It compares how FSDP and Muon manage the tradeoff between memory efficiency and communication cost, and why the “coalescing” approach can actually make training doable at scale.

:rocket: Part 2 — “No-BS Glossary: Distributed Training + The WHY Behind The How”

:backhand_index_pointing_right: Link here

Most distributed training docs tell you how things are done — DP, PP, TP, ZeRO, etc.
This glossary flips that: it starts from the why, then builds the how with mental models that make the acronyms stick.

It breaks down:

  • Data Parallelism (DP) – replicate the model, split the data

  • Pipeline Parallelism (PP) – split layers across GPUs

  • Tensor Parallelism (TP) – split computations inside layers

  • ZeRO & FSDP – how optimizer states, gradients, and parameters are sharded

If you’ve ever tried to read distributed systems code and felt lost in acronym soup, this is the mental map I wish I had when I started. :compass:

:nerd_face: Part 3 and beyond are still in progress.

:speech_balloon: I also started a Discord study group for anyone who wants to go through the “Muon is Scalable for LLM Training” paper together.
You can find the invite link and details here.

Would love to connect with others who are exploring distributed training, scaling laws, or building training infrastructure for large models.

#Muon #LLMTraining #Parallelism #DistributedSystems #ScalingLLMs #DeepLearning #OpenResearch #ShowAndTell #FSDP #ZeRO #OptimizerDesign #ExpertParallelism

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:rocket: Part 2 of “The Turtle Speed Breakthrough” is live! :turtle::sparkles:

Hey everyone! :waving_hand:

I just published the second installment of my Turtle Speed Breakthrough series — this one’s called:
:backhand_index_pointing_right: “The Blueprint for Distributed Chaos”

It continues from Part 1 where I unpacked how Muon’s optimizer architecture tackles the speed and memory bottlenecks of distributed training.

In Part 2, I get more hands-on and explore how the blueprint behind distributed training is actually implemented:

:pushpin: Here’s what it covers:

  • How each GPU finds its place in a 2D or 4D grid (DP, TP, PP, EP groups)

  • The logic behind creating distributed process groups — and why ordering matters :fire:

  • How parameters and gradients get sharded step-by-step using virtual “global buffers”

  • The subtle details that make ZeRO-style sharding work after tensor parallel slicing

I also shared annotated code, handwritten diagrams, and my own “aha” moments (plus a few :woman_facepalming: ones).

:nerd_face: Ever curious how frameworks like Megatron-LM, DeepSpeed, or FSDP coordinate chaos under the hood? This post walks through the common patterns behind those API calls.

:backhand_index_pointing_right: :link: Read it here: The “Turtle Speed” Breakthrough :turtle::sparkles: Part 2: The Blueprint for Distributed Chaos”

I’d love to hear your feedback — What tripped you up the most when learning distributed training?

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:turtle: The “Turtle Speed” Breakthrough, Part 3: My Map of the Distributed Nightmare is live :nerd_face:

Hi everyone!

I’ve just published Part 3 of my “Turtle Speed Breakthrough” series, where I continue decoding Moonshot AI’s paper “Muon is Scalable for LLM Training.”

In this part, I focus on the execution layer — how distributed chaos turns into a well-structured system:

  • How DP and TP all_gather calls interact

  • How parameters flow through the 4-step choreography (DP→TP→TP→DP)

  • The difference between “buffers,” “buckets,” and “virtual maps”

  • My handwritten notes and refactored annotated code

:blue_book: Part 1: Decoding ZeRO & FSDP
:blue_book: Part 2: The blueprint for distributed chaos
:blue_book: Part 3: The “Turtle Speed” Breakthrough 🐢✨, Part 3: My Map of the Distributed Nightmare | by Jennifer Wei | Oct, 2025 | Medium

This all began after my Muon optimizer tutorial unexpectedly took off with 700+ downloads — which inspired me to dig into how Muon could scale in production.

I’m also running a Discord study group on the Moonshot paper — if you’re interested in distributed optimizers, join us!
:backhand_index_pointing_right: BirdOfParadise

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