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Find skills for tasks involving NVIDIA products or GPU computing using the live catalog. Get up to three relevant recommendations, with reasons, installation commands, and starter prompts; installation requires explicit approval.

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• Find a skill for Jetson setup
• Discover skills for CUDA or GPU development
• Compare skills for NVIDIA video analysis workflows

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

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• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

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• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Set up and operate NVIDIA AI-Q Blueprint locally or on your own infrastructure. Choose a deployment mode, configure credentials, check service health, troubleshoot failures, and provide a verified server address for aiq-research.

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• Starting a backend for aiq-research
• Running AI-Q through a terminal or browser
• Deploying AI-Q to a Kubernetes cluster

Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, Isaac, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.

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Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.

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• Serving a language model through a chat completion API
• Processing many prompts in batches
• Configuring quantization to reduce memory use

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

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• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

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• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Plan NVIDIA NuRec workflows that turn sensor recordings into renderable 3D scenes. This routing skill selects the relevant specialist skills for dataset downloads, conversion, training, rendering, and frame cleanup, and puts the steps in order.

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• Plan a reconstruction workflow from sensor recordings
• Find the steps to render an existing USDZ scene
• Choose a workflow for cleaning up rendered frames

Send research questions to a running NVIDIA AI-Q Blueprint backend and receive reports with citations and source URLs preserved. Track longer research jobs, check their progress, and retrieve completed reports after a local interruption.

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• Research a topic through AI-Q
• Check progress on a long research job
• Resume tracking research after an interruption

Operate NVIDIA Nsight Graphics from the command line to capture application frames and GPU activity. Summarize performance traces, analyze existing capture files through ngfx-replay, and check installed versions and compatibility before starting an investigation.

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• Check installed Nsight Graphics versions and compatibility
• Capture frames from a graphics application
• Summarize GPU activity and investigate performance bottlenecks

Plan memory use and monitor long training jobs on NVIDIA DGX Spark. It guides troubleshooting for out-of-memory failures, tracks temperature and power alongside training logs, and helps assess competition between training and inference workloads.

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• Estimating available memory before training
• Troubleshooting out-of-memory failures
• Investigating slowdowns during long training jobs

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

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• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display

Guide the use of NVIDIA cuOpt for route planning and mathematical optimization, choosing values to minimize cost or maximize profit under constraints. Covers requirements, environment checks, installation guidance, and verification of solver results.

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• Clarify vehicle routing requirements and constraints
• Plan how to use cuOpt through Python, REST, C, or CLI
• Review installation and server setup options

NVIDIA Sync デスクトップアプリ。DGX Spark, DGX Station, SSH Linux ホストのリモート接続管理。mDNS 自動検出, 手動 IP, SSH config インポート, Tailscale, 自動ネットワーク切替。 Cluster Assistant, ConnectX-7 クラスタトポロジ, netplan 検証。 Resource Monitor (CPU / GPU / memory), release notes。 VS Code, VS Code Insiders, Cursor, NVIDIA AI Workbench 起動, カスタムアプリ, ポートフォワード, SSH トンネル。

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NVIDIA DGX Spark User Guide リファレンス。GB10 Grace Blackwell Superchip 仕様、 first-boot / UEFI、ConnectX-7 Networking (旧 Spark Stacking)、複数 Spark 接続、 NVIDIA Sync Cluster Assistant、DGX OS / NGC / container runtime、 Release Notes (DGX OS 7.5.0)、known issues、PXE / cloud-init、fleet 運用、 apt / fwupdmgr、recovery、PSIRT、 NVIDIA Developer Forums 由来 troubleshooting、 build.nvidia.com/spark playbook (vLLM / vLLM cluster / TensorRT-LLM / Ollama / ComfyUI / NIM / NeMo / VLM fine-tuning / NVIDIA PAIR / NCCL)、Tailscale、VS Code。

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Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

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• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Check ten recurring problems before or during machine learning runs on NVIDIA DGX Spark. Diagnose startup failures, out-of-memory errors, and falling throughput, with guidance on compatible environments, resource use, numeric formats, and training across two devices.

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• Checking a DGX Spark before a long training run
• Diagnosing memory errors despite apparent free capacity
• Investigating throughput drops during training

Coordinate CAD and source assets through conversion to USD, a 3D scene format, material and physics assignment, and SimReady checks. Produce stage reports, with preview rendering and validated delivery packages when requested.

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• Prepare CAD assets for simulation
• Convert source assets to USD and check basic validity
• Assign materials and physics properties

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

NVIDIA App MCP: drivers, games, laptops, overlay. Check drivers, manage and optimize games, configure laptop features.

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Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).

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