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Single node, multiple GPUs · nvidia | DecimalAI

documentationby nvidia

Single node, multiple GPUs

nvidia/cudaq-guide

Source nvidia/skills ↗License Apache-2.0

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

SkillScore95Maintained

Automatic

Coding pack

Last verified 8/12/2026

What it changes

Passed 21 of 22 test cases with the skill, and 14 of 22 without it.

Without skill64%

With skill95%

Checks it passed that the model alone missed

Recommends executing pip install cudaq.
Directs the user to the cudaq-compiler topic.
Recommends setting the emulate flag to True during local testing.

and 4 more in the full report

7 fixed14 passed either way1 still failing0 broke

Pass-rate lift: +32 pp
Tokens: +98%
Agent turns: 0%
Response time: -51%

Model gemini-3.6-flashMeasured 8/12/2026v121 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/nvidia-cudaq-guide/SKILL.md https://app.decimal.ai/s/nvidia-cudaq-guide/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add nvidia-cudaq-guide, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/nvidia-cudaq-guide/ and pins it in .decimal/skills.lock

decimalai skills add nvidia-cudaq-guide

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/nvidia-cudaq-guide/SKILL.md https://app.decimal.ai/s/nvidia-cudaq-guide/SKILL.md

Raw file: /s/nvidia-cudaq-guide/SKILL.md ↗ · machine-readable summary at /s/nvidia-cudaq-guide.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 7

Prompt

I need to inspect how the CUDA-Q AST bridge converts Python source constructs into Quake MLIR and QIR representations during JIT compilation. Where should I route this request?

What was checked, without and with the skill
Checked	Without skill	With skill
Directs the user to the cudaq-compiler topic.	Missed	Passed

case-08 · gemini-3.6-flash · 8/12/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md297 linessingle file

CUDA-Q Getting Started Guide

You are a CUDA-Q expert assistant. Use $ARGUMENTS with the routing table below to jump straight to the topic the user needs.

Purpose

Guide users through the CUDA-Q platform: installation, writing quantum kernels, GPU-accelerated simulation, connecting to QPU hardware, and exploring built-in applications.

Prerequisites

Python 3.10+ (for Python installation path)
CUDA Toolkit (for GPU-accelerated targets on Linux; not required on macOS)
NVIDIA GPU (optional; CPU-only simulation available via qpp-cpu)
For C++ path: Linux or WSL on Windows
For QPU access: provider-specific credentials and account

Instructions

Invoke with /cudaq-guide [argument]
If no argument is given, display the full onboarding menu and ask what the user wants to explore
Pass an argument from the routing table below to jump directly to that topic
Read local CUDA-Q documentation files to answer questions accurately

References

Section	Doc file
Install	`docs/sphinx/using/install/install.rst`, `docs/sphinx/using/quick_start.rst`
Test Program	`docs/sphinx/using/basics/kernel_intro.rst`, `docs/sphinx/using/basics/build_kernel.rst`
GPU Simulation	`docs/sphinx/using/backends/sims/svsims.rst`, `docs/sphinx/using/examples/multi_gpu_workflows.rst`
QPU	`docs/sphinx/using/backends/hardware.rst`, `docs/sphinx/using/backends/cloud.rst`
Applications	`docs/sphinx/using/applications.rst`
Parallelize	`docs/sphinx/using/examples/multi_gpu_workflows.rst`

Routing by Argument

Track record

Benchmark

21 / 22

test cases passed, vs 14 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

nvidia / skills

skills/cudaq-guide/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.