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Node wrapper around nvidia-smi.
Tailwind CSS plugin for the NVIDIA Design Language, providing utility classes and theme configuration
n8n community node for NVIDIA NIM - Chat completions and image analysis with NVIDIA AI models
SLEK AI CLI — Powered by NVIDIA API
NVIDIA NIM Chat Model for n8n
n8n community node for NVIDIA NIM
Deploy validated NVIDIA AI Cluster Runtime (AICR) recipes for GPU-accelerated Kubernetes clusters.
Copyright (c) 2019 NVIDIA Corporation. All rights reserved.
NVIDIA NIM API provider extension for pi coding agent — access 100+ models from build.nvidia.com
High-performance speech-to-text inference addon using NVIDIA Parakeet models for Bare runtime
WebGPU speech recognition for NVIDIA Parakeet in the browser, powered by ONNX Runtime Web.
Javascript/WASM bindings for Nvidia PhysX 5.6.1
OpenClaw sandbox backend for the NVIDIA OpenShell CLI with mirrored local workspaces and SSH command execution.
Create new Elements projects with 'npm create @nvidia-elements'. A thin wrapper that delegates to @nvidia-elements/cli for project scaffolding.
Claude Code provider selector for Anthropic, Ollama, Ollama Cloud, DeepSeek.com, OpenCode, LM Studio, vLLM, NVIDIA hosted, and self-hosted NIM.
MCP server to search across NVIDIA blogs and releases to empower LLMs to better answer NVIDIA specific queries
NVIDIA SMI wrapper to get NVIDIA GPU info
CORTEX — Autonomous AGI Terminal. Any LLM, one command. NVIDIA, OpenAI, Gemini, Groq, Ollama and more.
NVIDIA NIM Expert Orchestrator with MCP support. A multi-model consensus and synthesis engine.
VATSIM & VATPRC 活动监控、订阅、定时提醒、NVIDIA 翻译
All-in-one AI CLI with Dust, Cerebras, Devin, NVIDIA, OmniRoute - beautiful TUI
Helps to monitor Nvidia GPU utilization using nvidia-smi
Integración de NVIDIA Llama4 con LangChain.js, con soporte para Tools Agent de n8n
A Rust utility to retrieve information about NVIDIA GPUs installed in your system.
Prometheus exporter for NVIDIA GPUs using NVML
Command-line utility for monitoring GPU hardware. It provides a real-time view of GPU utilization, memory usage, temperature, power consumption, and other metrics.
Check CLI for NVIDIA software environment
NVIDIA NeMo LLM integration for AI-powered translation
A terminal-based system resource monitor — GPU-aware, Braille charts, per-char label inversion
Code analysis agent for Python compatibility assessment
Build agent for compiling Rust to WASM
CLI for Portalis - GPU-accelerated Python to Rust/WASM transpiler
Core library for the Portalis Python to Rust/WASM transpiler
CUDA GPU acceleration bridge for parallel processing
Python code ingestion and parsing agent
Get informations about the system's GPU(s), through nvidia-smi.
Sensu nvidia plugins
SGC-Ruby-CUDA implements Ruby bindings to Nvidia CUDA SDK. It provides easy access to CUDA-enabled GPU from a Ruby program.
Write Ruby extension in C/C++ with NVIDIA CUDA. A simple wrapper command for nvcc and a monkey patch for mkmf.
KappaConfig is a module to give easy access to NVIDIA CUDA from Ruby using the Kappa Library.
Minimal Ruby bindings for NVIDIA TensorRT inference using Rice
RUIC is a library that understands the XML formats used by NVIDIA's "UI Composer" tool suite. In addition to APIs for analyzing and manipulating these files—the UIC portion of the library—it also includes a mini DSL for writing scripts that can be run by the `ruic` interpreter.
ignis-autograd adds a differentiable tensor (Ignis::Tensor) and a reverse-mode autograd tape on top of the Ignis GPU foundation. Build computation graphs over GPU arrays and get exact gradients (verified against finite differences). The building block for neural-network training in pure Ruby on an NVIDIA GPU.
Ignis is the foundation of a CUDA-backed deep-learning ecosystem for Ruby that actually targets native Windows. It provides a GPU n-dimensional array (Ignis::NDArray), CUDA memory/device management, a runtime kernel compiler (NVRTC) with a batteries-included kernel library, fp16/bf16 conversion, and cuBLAS GEMM. Kernels are compiled at runtime and libraries are bound via FFI — there are NO C extensions, so installation needs no compiler or devkit (the usual Windows native-gem killer). Requires an NVIDIA GPU + CUDA toolkit/runtime.
2020年代、ビッグデータをどう扱えばよいか。今は各プロダクト毎に効率的な扱い方を実装していますが、2020年代はそんな時代ではありません！ビッグデータの扱いでも、共通で必要なものはプロダクトを超えて協力して開発して共有する、そんな時代です！ビッグデータのための共通基盤、それがオープンソースのApache Arrowです。AmazonもGoogleもNVIDIAも開発に参加しています。 このセッションではApache Arrow開発チームの主要メンバーがApache Arrowフォーマットがなぜ速いのかを説明します。
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