Add Model
guoqingbao/xinfer
Adapt and port new LLM model architectures to this xinfer project.
Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models.
Install Megatron Memory Estimator
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install yzlnew/infra-skills megatron-memory-estimator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/megatron-memory-estimator .claude/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .claude/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimatorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install yzlnew/infra-skills megatron-memory-estimator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/megatron-memory-estimator .agents/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .agents/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install yzlnew/infra-skills megatron-memory-estimator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/megatron-memory-estimator .cursor/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .cursor/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/yzlnew/infra-skills.git --path megatron-memory-estimator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install yzlnew/infra-skills megatron-memory-estimator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/megatron-memory-estimator .gemini/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .gemini/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install yzlnew/infra-skills megatron-memory-estimatorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/megatron-memory-estimator .github/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .github/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install yzlnew/infra-skills megatron-memory-estimator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/yzlnew/infra-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/megatron-memory-estimator .opencode/skills/megatron-memory-estimator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "megatron-memory-estimator" agent skill from https://github.com/yzlnew/infra-skills/tree/main/megatron-memory-estimator into .opencode/skills/megatron-memory-estimator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "megatron-memory-estimator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
megatron-memory-estimator- GitHub stars
- 149
- Token cost
- ~2.2k tokens
- SKILL.md length
- 492 words
- Files
- 13 (incl. scripts, references)
- Skills in repo
- 8
- Repo updated
- First seen
- Licence
- None found
At a glance
Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models.
- Works in 5 steps: Enable Distributed Optimizer (included… → Activation Recomputation… → Increase Expert Parallelism (MoE only)… → …
- Estimate memory from HuggingFace model configs (DeepSeek-V3
- SKILL.md covers Quick Start, Available Scripts, Common Workflows and Understanding Output, plus 6 more sections
- Runs Python and Shell scripts from its folder; calls python and pip; reaches github.com
What it does
Megatron Memory Estimator is an agent skill from yzlnew/infra-skills. Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models. Use when users need to (1) estimate memory from HuggingFace model configs (DeepSeek-V3, Qwen, etc.), (2) plan GPU resource allocation for training, (3) compare different parallelism strategies (TP/PP/EP/CP), (4) determine if a model fits in available GPU memory, or (5) optimize training configurations for memory efficiency.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts and reference files (for example `megatron_memory_estimator/__init__.py`, `megatron_memory_estimator/estimate_013.py` and `megatron_memory_estimator/moe_mem_estimator/__init__.py`).
It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with NVIDIA AI Platform, Hugging Face, DeepSeek and Qwen. The repository describes itself as: A collection of specialized agent skills for AI infrastructure development, enabling Claude Code to write, optimize, and debug high-performance systems.
When your agent uses it
- Estimate memory from HuggingFace model configs (DeepSeek-V3
- Plan GPU resource allocation for training
- Compare different parallelism strategies (TP/PP/EP/CP)
- Determine if a model fits in available GPU memory
Example prompts
- “/megatron-memory-estimator”
Requirements
- Python 3
- A Bash shell
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Enable Distributed Optimizer (included by default)
- Activation Recomputation (--recompute-granularity full)
- Increase Expert Parallelism (MoE only) (--ep N)
- Increase Pipeline Parallelism (--pp N)
- Reduce Batch Size (--micro-batch-size 1)
What it can do on your machine
Read from SKILL.md and the folder at commit f3a8d7d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python and Shell), which the agent can run.
Shell commands in SKILL.md call:
pythonpip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Megatron Memory Estimator loads about 2.2k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 492 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
Without a licence we can't republish the file, so here is its outline and opening line. It has 492 words (~2,224 tokens).
“Estimate GPU memory usage for Megatron-based models directly from HuggingFace configs or custom specifications.”
- name
- megatron-memory-estimator
Files
SKILL.md and 12 other files (scripts, references) in megatron-memory-estimator of yzlnew/infra-skills.
- SKILL.md
- megatron_memory_estimator/.gitattributes
- megatron_memory_estimator/.gitignore
- megatron_memory_estimator/__init__.py
- megatron_memory_estimator/estimate_013.py
- megatron_memory_estimator/moe_mem_estimator/__init__.py
- megatron_memory_estimator/moe_mem_estimator/base.py
- megatron_memory_estimator/moe_mem_estimator/gpt_model.py
- megatron_memory_estimator/moe_mem_estimator/layers.py
- references/configuration_guide.md
- references/parallelism_strategies.md
- scripts/estimate_from_hf.py
- scripts/setup_env.sh
Open the folder on GitHubat commit f3a8d7d
Compare with similar skills
Megatron Memory Estimator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Megatron Memory Estimator this skillyzlnew/infra-skills | 149 | — | ~2.2k | Automated safety check: Pass | None | |
| Add Modelguoqingbao/xinfer | 334 | — | ~4.2k | Automated safety check: Notes | MIT | |
| Setup Benchmark Inputsmlc-ai/pith-train | 355 | — | ~399 | Automated safety check: Pass | Apache-2.0 | |
| Qwen Mtp GgufR6410418/Jackrong-llm-finetuning-guide | 1.7k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Veomni New ModelByteDance-Seed/VeOmni | 2.2k | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Resolvealexziskind1/model-shelf | 130 | — | ~792 | Automated safety check: Pass | MIT |
Similar skills
Setup Benchmark Inputs
mlc-ai/pith-train
Set up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain.
Qwen Mtp Gguf
R6410418/Jackrong-llm-finetuning-guide
Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.
Veomni New Model
ByteDance-Seed/VeOmni
A skill your agent uses when adding support for a new model to VeOmni.
Resolve
alexziskind1/model-shelf
Always resolve Hugging Face models via model-shelf before any download.
Check Model
guoqingbao/xinfer
Check model compatibility with xinfer before loading. An agent skill from guoqingbao/xinfer.
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Categories
Questions about Megatron Memory Estimator
What does Megatron Memory Estimator do?
Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models. Megatron Memory Estimator is an agent skill from yzlnew/infra-skills. Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models.
When should I use Megatron Memory Estimator?
Megatron Memory Estimator fits situations like: estimate memory from HuggingFace model configs (DeepSeek-V3; plan GPU resource allocation for training; compare different parallelism strategies (TP/PP/EP/CP); determine if a model fits in available GPU memory.
How do I install Megatron Memory Estimator in Claude Code?
Run `npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a claude-code`. Or copy the skill folder (megatron-memory-estimator in yzlnew/infra-skills) into .claude/skills/megatron-memory-estimator in your project. Claude Code loads it when a task matches its description.
How do I install Megatron Memory Estimator in Codex?
Run `npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a codex`. Or copy the skill folder (megatron-memory-estimator in yzlnew/infra-skills) into .agents/skills/megatron-memory-estimator in your project. Codex loads it when a task matches its description.
Can I use Megatron Memory Estimator in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yzlnew/infra-skills --skill megatron-memory-estimator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/megatron-memory-estimator, .gemini/skills/megatron-memory-estimator, .github/skills/megatron-memory-estimator and .opencode/skills/megatron-memory-estimator in your project.
What does Megatron Memory Estimator need to run?
Going by SKILL.md and its folder, Megatron Memory Estimator needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3; A Bash shell.
Does Megatron Memory Estimator access the network?
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Megatron Memory Estimator safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Megatron Memory Estimator use?
No licence was found for Megatron Memory Estimator or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Megatron Memory Estimator use?
About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.7k tokens, read only when the agent opens those files.
What are the alternatives to Megatron Memory Estimator?
Skills that share tags, products or a category with Megatron Memory Estimator: Add Model (guoqingbao/xinfer, 334 stars), Setup Benchmark Inputs (mlc-ai/pith-train, 355 stars), Qwen Mtp Gguf (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars) and Veomni New Model (ByteDance-Seed/VeOmni, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Megatron Memory Estimator?
yzlnew (a GitHub user) maintains it in yzlnew/infra-skills, which has 149 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on July 9, 2026.
Source: yzlnew/infra-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.