Official agent skill

Nemo Mbridge Perf Cpu Offloading

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Nemo Mbridge Perf Cpu Offloading

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .claude/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .claude/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading

Type 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .agents/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .agents/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .cursor/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .cursor/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-mbridge-perf-cpu-offloading

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .gemini/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .gemini/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading

Installs 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).

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .github/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .github/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-cpu-offloading --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/nemo-mbridge-perf-cpu-offloading .opencode/skills/nemo-mbridge-perf-cpu-offloading && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nemo-mbridge-perf-cpu-offloading" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-cpu-offloading into .opencode/skills/nemo-mbridge-perf-cpu-offloading/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-cpu-offloading", 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: nemo-mbridge-perf-cpu-offloading
GitHub stars: 3.5k
Token cost: ~2.3k tokens
SKILL.md length: 575 words
Files: 6
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

AI & LLM Engineering work in your project
SKILL.md covers References, What It Is, Quick Decision and Enablement, plus 7 more sections
Calls uv

What it does

Nemo Mbridge Perf Cpu Offloading is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `BENCHMARK.md`, `card.yaml` and `evals/evals.json`).

It sits in AI & LLM Engineering. It works with NVIDIA AI Platform and CUDA. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

AI & LLM Engineering work in your project

Example prompts

“/nemo-mbridge-perf-cpu-offloading”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. 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
Shell commands in SKILL.md call:
- uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
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

Nemo Mbridge Perf Cpu Offloading loads about 2.3k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 575 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~50
When it runs · the whole SKILL.md, loaded when a task matches: ~2.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 575 words, ~2,329 tokens.

Download SKILL.mdSave it as .claude/skills/nemo-mbridge-perf-cpu-offloading/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: nemo-mbridge-perf-cpu-offloading
description: Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.
license: Apache-2.0
when_to_use: Enabling CPU offload to reduce GPU memory, or investigating a commit that changed CPU offloading config and caused OOM or a crash; 'cpu_offloading'…

CPU Offloading

References

Stable docs: @docs/training/cpu-offloading.md
Structured metadata: @skills/nemo-mbridge-perf-cpu-offloading/card.yaml

What It Is

Two independent mechanisms to move data from GPU to CPU memory:

Mechanism	Config namespace	What gets offloaded	PP restriction
Activation offloading	`model.cpu_offloading*`	Activations (and optionally weights) per transformer layer	PP must be 1
Optimizer offloading	`optimizer.optimizer_cpu_offload`	Adam optimizer states (momentum + variance) via `HybridDeviceOptimizer`	None

Quick Decision

Situation	Recommendation
Large MoE model (30B+), needs PP > 1	Optimizer offloading — activation offloading is blocked by PP=1
Small/medium model, PP=1 fits, activation memory dominates	Activation offloading
Want tunable memory-speed tradeoff	Optimizer offloading with fractional `optimizer_offload_fraction`
Throughput is top priority	Don't enable — offloading always adds overhead
CUDA graphs are needed	Only optimizer offloading — activation offloading is incompatible
Memory pressure is moderate	Optimizer offload at 25–50% fraction for best efficiency

Enablement

Optimizer CPU offloading (recommended for large models)

python

cfg.optimizer.optimizer_cpu_offload = True
cfg.optimizer.optimizer_offload_fraction = 1.0
cfg.optimizer.overlap_cpu_optimizer_d2h_h2d = True

CLI overrides:

bash

optimizer.optimizer_cpu_offload=True \
optimizer.optimizer_offload_fraction=0.5 \
optimizer.overlap_cpu_optimizer_d2h_h2d=True

Activation CPU offloading (small/medium models only)

python

cfg.model.cpu_offloading = True
cfg.model.cpu_offloading_num_layers = 16
cfg.model.cpu_offloading_activations = True
cfg.model.cpu_offloading_weights = False

cfg.model.pipeline_model_parallel_size = 1
cfg.model.recompute_granularity = None
cfg.model.cuda_graph_impl = "none"

Config Parameter Reference

Optimizer offloading

Parameter	Default	Description
`optimizer_cpu_offload`	`False`	Master switch
`optimizer_offload_fraction`	`0.0`	Fraction of optimizer states on CPU (0.0–1.0)
`overlap_cpu_optimizer_d2h_h2d`	`False`	Overlap GPU↔CPU transfers with compute
`use_torch_optimizer_for_cpu_offload`	`False`	Use `torch.optim` instead of fused optimizer for CPU portion

Activation offloading

Parameter	Default	Description
`cpu_offloading`	`False`	Master switch
`cpu_offloading_num_layers`	`0`	Number of transformer layers to offload (0 to num_layers-1)
`cpu_offloading_activations`	`True`	Offload activations
`cpu_offloading_weights`	`False`	Offload weights
`cpu_offloading_double_buffering`	`False`	Double-buffer across layers while reloading

Compatibility And Constraints

Activation offloading

pipeline_model_parallel_size must be 1
recompute_granularity must be None
Cannot combine with fine_grained_activation_offloading
Cannot combine with CUDA graphs
cpu_offloading_num_layers must be in [0, num_layers-1)

Optimizer offloading

Requires use_distributed_optimizer = True (default in most recipes)
No PP, recompute, or CUDA graph restrictions
optimizer_offload_fraction must be in [0.0, 1.0]

Practical: large MoE models

Activation offloading is blocked for Qwen3-30B-A3B and similar large MoE models. The PP=1 constraint means each GPU holds all 48 layers; model weights + optimizer states alone (~70 GB) exceed H100 80 GB capacity.

Minimal Runnable Command

bash

uv run python scripts/training/run_recipe.py \
  --recipe qwen3_30b_a3b_pretrain_config \
  optimizer.optimizer_cpu_offload=True \
  optimizer.optimizer_offload_fraction=0.5 \
  train.train_iters=20 \
  train.global_batch_size=8 \
  train.micro_batch_size=1

Verification

Unit tests

bash

uv run python -m pytest \
  tests/unit_tests/models/test_gpt_full_te_layer_autocast_spec.py -k "cpu_offload" \
  tests/unit_tests/peft/test_utils.py -k "cpu_offload" -q

Success criteria

Config validation passes for the selected offloading mode
Training completes without OOM or NCCL errors
Loss matches the non-offloaded baseline (max delta < 0.001)
Memory usage drops proportionally to offload fraction

Code Anchors

Show full SKILL.md (232 more words)Show less

MCore activation offload constraints

129613103rdpartyMega

        if self.cpu_offloading and (
            self.cpu_offloading_num_layers < 0 or self.cpu_offloading_num_layers >= self.num_layers
        ):
            raise ValueError(...)

        if self.cpu_offloading and self.pipeline_model_parallel_size > 1:
            raise ValueError(
                "Currently there is no support for Pipeline parallelism with CPU offloading"
            )

        if self.cpu_offloading and self.recompute_granularity is not None:
            raise ValueError(
                "CPU offloading does not work when activation recomputation is enabled"
            )

MCore CUDA graph incompatibility

194319443rdpartyMega

            if self.cpu_offloading:
                raise ValueError("CUDA graphs not supported with CPU offloading.")

MCore fine-grained offloading mutual exclusion

142714303rdpartyMega

        if self.fine_grained_activation_offloading:
            assert (
                not self.cpu_offloading
            ), "fine_grained_activation_offloading cannot be enabled with cpu_offloading."

MCore HybridDeviceOptimizer instantiation

4805183rdpartyMegatr

        if config.optimizer_cpu_offload:
            # ... setup cpu/gpu optimizer classes ...
            optimizer = HybridDeviceOptimizer(
                param_groups,
                offload_fraction=config.optimizer_offload_fraction,
                cpu_optimizer_cls=cpu_optimizer_cls,
                gpu_optimizer_cls=gpu_optimizer_cls,
                overlap_cpu_optimizer_d2h_h2d=config.overlap_cpu_optimizer_d2h_h2d,
                pin_cpu_grads=config.pin_cpu_grads,
                pin_cpu_params=config.pin_cpu_params,
            )

Bridge CUDA graph guard

232234srcmegatronbri

        assert not config.cpu_offloading and config.recompute_granularity is None, "Cudagraphs not supported"

Bridge activation offloading in PEFT

621631srcmegatronbri

        if self.config.cpu_offloading and self.config.cpu_offloading_activations:
            x.activation_offloading = True
        x, _ = self.linear_in(x)
        x = self.activation(x)
        if self.config.cpu_offloading and self.config.cpu_offloading_activations:
            x.activation_offloading = True
        x, _ = self.linear_out(x)

Failure Diagnosis

Symptom	Likely Cause	How To Confirm	Fix
`Currently there is no support for Pipeline parallelism with CPU offloading`	Activation offload + PP > 1	Check `pipeline_model_parallel_size`	Set PP=1 or use optimizer offloading
`CPU offloading does not work when activation recomputation is enabled`	Activation offload + recompute	Check `recompute_granularity`	Set `recompute_granularity=null`
`fine_grained_activation_offloading cannot be enabled with cpu_offloading`	Both offloading modes enabled	Check both flags	Use one or the other
`CUDA graphs not supported with CPU offloading`	CUDA graphs + activation offload	Check `cuda_graph_impl`	Set `cuda_graph_impl="none"`
OOM with activation offloading	Model too large for PP=1	Check allocated memory vs 80 GB	Use optimizer offloading with PP > 1
Extreme slowdown (>4x)	100% optimizer offload, CPU Adam bottleneck	Compare iter time at different fractions	Reduce fraction or enable `overlap_cpu_optimizer_d2h_h2d`
OOM at partial optimizer offload	Insufficient offload for this config	Check memory at different fractions	Increase fraction or add PP

Known Limitations

Activation offloading requires PP=1, making it impractical for large models (30B+ MoE) that need pipeline parallelism.
Optimizer offloading throughput penalty scales linearly (~1.9x at 25%, ~4.2x at 100% for Qwen3-30B-A3B).
D2H/H2D overlap provides only ~7% speedup because CPU Adam compute is the dominant bottleneck.
fine_grained_activation_offloading is a separate module-level approach that works with PP > 1 but cannot be combined with layer-level cpu_offloading.

Files

SKILL.md and 5 other files in skills/nemo-mbridge-perf-cpu-offloading of NVIDIA/skills.

SKILL.md
BENCHMARK.md
card.yaml
evals/evals.json
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 67a13c0

Compare with similar skills

Nemo Mbridge Perf Cpu Offloading 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.

Nemo Mbridge Perf Cpu Offloading compared with similar skills
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Nemo Mbridge Perf Cpu Offloading this skillNVIDIA/skills	3.5k	—	~2.3k	Automated safety check: Pass	Apache-2.0	today
Graphsignalgraphsignal/graphsignal	257	—	~6.2k	Automated safety check: Pass	Apache-2.0	9 days ago
LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS	911	—	~2.8k	Automated safety check: Pass	None	2 days ago
Optimize OpCVCUDA/CV-CUDA	2.7k	—	~834	Automated safety check: Pass	Custom licence	21 days ago
Cutlass SkillslowlyC/agent-gpu-skills	169	—	~1.3k	Automated safety check: Pass	MIT	2 mo ago
Setup Workshop Nemoclawbrevdev/workshop-build-an-agent	144	—	~5.2k	Automated safety check: Pass	Apache-2.0	today

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Works with

Questions about Nemo Mbridge Perf Cpu Offloading

What does Nemo Mbridge Perf Cpu Offloading do?

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer. Nemo Mbridge Perf Cpu Offloading is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

When should I use Nemo Mbridge Perf Cpu Offloading?

Nemo Mbridge Perf Cpu Offloading fits situations like: AI & LLM Engineering work in your project.

How do I install Nemo Mbridge Perf Cpu Offloading in Claude Code?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a claude-code`. Or copy the skill folder (skills/nemo-mbridge-perf-cpu-offloading in NVIDIA/skills) into .claude/skills/nemo-mbridge-perf-cpu-offloading in your project. Claude Code loads it when a task matches its description.

How do I install Nemo Mbridge Perf Cpu Offloading in Codex?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a codex`. Or copy the skill folder (skills/nemo-mbridge-perf-cpu-offloading in NVIDIA/skills) into .agents/skills/nemo-mbridge-perf-cpu-offloading in your project. Codex loads it when a task matches its description.

Can I use Nemo Mbridge Perf Cpu Offloading 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 NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemo-mbridge-perf-cpu-offloading, .gemini/skills/nemo-mbridge-perf-cpu-offloading, .github/skills/nemo-mbridge-perf-cpu-offloading and .opencode/skills/nemo-mbridge-perf-cpu-offloading in your project.

What does Nemo Mbridge Perf Cpu Offloading need to run?

Going by SKILL.md and its folder, Nemo Mbridge Perf Cpu Offloading needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Nemo Mbridge Perf Cpu Offloading access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nemo Mbridge Perf Cpu Offloading 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. Review the folder before installing.

What licence does Nemo Mbridge Perf Cpu Offloading use?

Nemo Mbridge Perf Cpu Offloading is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemo Mbridge Perf Cpu Offloading use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemo Mbridge Perf Cpu Offloading?

Skills that share tags, products or a category with Nemo Mbridge Perf Cpu Offloading: Graphsignal (graphsignal/graphsignal, 257 stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 911 stars), Optimize Op (CVCUDA/CV-CUDA, 2.7k stars) and Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemo Mbridge Perf Cpu Offloading?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Nemo Mbridge Perf Cpu Offloading

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

CPU Offloading

References

What It Is

Quick Decision

Enablement

Optimizer CPU offloading (recommended for large models)

Activation CPU offloading (small/medium models only)

Config Parameter Reference

Optimizer offloading

Activation offloading

Compatibility And Constraints

Activation offloading

Optimizer offloading

Practical: large MoE models

Minimal Runnable Command

Verification

Unit tests

Success criteria

Code Anchors

MCore activation offload constraints

MCore CUDA graph incompatibility

MCore fine-grained offloading mutual exclusion

MCore HybridDeviceOptimizer instantiation

Bridge CUDA graph guard

Bridge activation offloading in PEFT

Failure Diagnosis

Known Limitations

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Nemo Mbridge Perf Cpu Offloading