Awesome NVIDIA NIM

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A curated list of resources, tools, projects, and workflows for building with NVIDIA NIM — inference microservices for deploying AI models at production scale.

NVIDIA NIM provides optimized, containerized AI model inference via a simple REST API. This list covers everything from getting started to advanced agentic workflows.

Inclusion criteria: NVIDIA NIM must be central to the project — the primary inference/embedding backend, not one of many interchangeable LLM providers. See Contributing for the full quality bar.

Submit a PR to add your project!

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Contents

• Official Resources
• Getting Started
• Models Available on NIM
• Agentic Workflows & Multi-Agent Systems
• RAG Pipelines
• Speech, Voice & Healthcare
• CLI Tools & SDKs
• Integrations
• Example Projects
• Tutorials & Blog Posts
• Community & Discussion
• Contributing

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Official Resources

• NVIDIA NIM Overview — Official landing page and API key signup.
• NIM API Catalog — Browse all hosted models (LLMs, VLMs, embeddings, speech, code).
• NVIDIA Generative AI Examples — Official RAG and agentic workflow examples, including a Community Showcase of external NIM projects.
• NVIDIA AI Blueprints — Production-ready, enterprise reference architectures using NIM.
• NIM Documentation — Full API reference and deployment guides.
• Metropolis NIM Workflows — Visual AI agent workflows using NIM microservices.
• NVIDIA NIM Deploy — Official YAML/Helm/Operator reference implementations for deploying NIM in Kubernetes and production.
• NVIDIA K8s NIM Operator — Operator for deploying and maintaining NIMs and NeMo microservices on Kubernetes.
• NVIDIA NIM Anywhere — Reference project to accelerate Gen AI development with NIM + AI Workbench, with or without local GPUs.

Getting Started

• NIM Quickstart Guide — Get your first API call working in under 5 minutes.
• NIM Free Credits — Free credits for new accounts; no GPU required.
• Deploy Your Own LLM with NIM — Containerized deployment blueprint for custom models.

Models Available on NIM

NIM hosts a large and growing catalog of models. Key categories:

LLMs

• meta/llama-3.3-70b-instruct — Meta's flagship instruction-tuned model.
• nvidia/nemotron-4-340b-instruct — NVIDIA's frontier reasoning model.
• mistralai/mistral-7b-instruct-v0.3 — Efficient 7B instruction model.
• microsoft/phi-3-mini-128k-instruct — Long-context small model.
• deepseek-ai/deepseek-r1 — Reasoning-focused model.

Code

• qwen/qwen2.5-coder-32b-instruct — Top-tier code model.
• ibm/granite-34b-code-instruct — Enterprise code model.

Embeddings

• nvidia/nv-embedqa-e5-v5 — High-quality retrieval embeddings.
• baai/bge-m3 — Multilingual embedding model.

Vision / Multimodal

• microsoft/phi-3-vision-128k-instruct — Vision + language understanding.
• nvidia/neva-22b — NVIDIA's VLM.

Full catalog: build.nvidia.com/explore/discover (model availability changes frequently — always check the live catalog).

Agentic Workflows & Multi-Agent Systems

• autobots ⭐ — PyPI-published CLI for spinning up NVIDIA NIM-powered multi-agent swarms. Live model catalog discovery, skill injection, extensive test suite. pip install autobot-swarm
• NVIDIA NeMo Agent Toolkit — Official open-source toolkit for connecting and optimizing agent teams, with MCP/A2A protocol support and NIM as a core backend.
• cybersentry — Autonomous AI-powered ethical hacking agent powered by Llama 3.1 70B on NVIDIA NIM.
• Blacknode — MCP-native visual workflow framework for AI agents with a node editor and Python export; NIM-supported.
• proteus *new* — Five-agent JD-aware resume analyzer with NIM pipeline: parse, calibrate, map, rewrite, draft.
• CodeSage *new* — AI codebase examiner for interview prep using 5 specialized NIM models for question generation and scoring.

RAG Pipelines

• NVIDIA RAG Blueprint — Official enterprise reference RAG pipeline using NIM microservices, NeMo Retriever, and guardrailing.
• RAG with NIM + LangChain — Production RAG using NIM embeddings + LLM inference.
• Hybrid RAG Workbench Example — RAG project that works interchangeably with build.nvidia.com endpoints, local NIM containers, or HF TGI.
• NVIDIA ChatRTX — Personalize a local LLM with your own docs using RAG + TensorRT-LLM + NIM, runs on RTX PCs.
• Chat-RAG — Gradio-based coding assistant streaming responses, pulling context from GitHub repos and local files, with NIM inference.
• nvidia-NIM-RAG — Community project demonstrating a NIM-powered RAG pipeline end to end.
• NVIDIA_NIM-demo — Streamlit RAG chatbot using NIM embeddings, FAISS, and document loaders.
• ella *new* — Medical triage RAG engine with 90K+ clinical chunks, NIM embeddings + Pinecone. pip install ella-sdk

Speech, Voice & Healthcare

• Nemotron Voice Agent Blueprint — Official NVIDIA blueprint for real-time, interruptible voice agents combining Nemotron ASR, LLM, and TTS in a streaming pipeline.
• nemotron-speech-demos — Vertical voice-agent examples (wire transfer with Twilio, ambient healthcare agents + guardrails) built on Nemotron NIM.
• NVIDIA Digital Biology Examples — Official BioNeMo + NIM examples for drug discovery: protein structure prediction, molecular generation, and genomics inference.
• Speech & Translation NIM Microservices Guide — Official walkthrough integrating ASR/NMT/TTS NIMs into a voice-driven RAG pipeline.

CLI Tools & SDKs

• autobot-swarm — CLI tool for NIM multi-agent orchestration. Live model discovery, skill injection, swarm configuration. pip install autobot-swarm
• pi-nvidia-nim — NVIDIA NIM provider extension for the pi coding agent; access 100+ models from build.nvidia.com with reasoning-level support.
• openai Python SDK — Works out-of-the-box with NIM (set base_url="https://integrate.api.nvidia.com/v1").
• ella-sdk *new* — Python SDK for medical triage RAG using NIM embeddings + Pinecone. pip install ella-sdk

Integrations

Frameworks

• LangChain NIM Integration — ChatNVIDIA class; drop-in for any LangChain chain or agent.
• LlamaIndex NIM — Use NIM as LLM or embedding provider in LlamaIndex pipelines.
• Haystack NIM — NVIDIA NIM components for Haystack pipelines.
• DSPy + NIM — Use NIM as the LM backend for DSPy programs.

Vector Stores

• Milvus — NVIDIA NIM + Milvus for large-scale vector search.
• Weaviate — NIM embedding and generative modules.

Example Projects

• autobots — Multi-agent CLI swarm using NIM with live model catalog discovery. PyPI-published.
• nextjs-nvidia-chatbot — Open-source AI chatbot template built with Next.js, the Vercel AI SDK, and NIM inference.
• H.E.I.M.D.A.L.L — Fleet telemetry natural-language insights using GPU data loading (cuDF), local LLM inference, and production NIM on GKE.
• Satark-AI — Multi-model deepfake detection and speaker verification platform using NIM (Llama 3.2-90B Vision) for image detection.
• Comfy-Cozy — AI co-pilot for ComfyUI with 129 MCP tools; supports NIM alongside other LLM providers.
• AETHER — Autonomous pentest platform built on LangGraph + NIM + Playwright.
• ella *new* — Medical triage RAG engine with 90K+ clinical chunks, NIM embeddings + Pinecone + Groq for zero-hallucination patient triage.
• proteus *new* — Five-agent JD-aware resume analyzer: semantic match scoring, gap analysis, bullet rewrites, and cover letters via NIM pipeline.
• CodeSage *new* — AI codebase examiner for viva/interview prep using 5 specialized NIM models (embed, rerank, question gen, scoring, safety).

Tutorials & Blog Posts

• Getting Started with NVIDIA NIM APIs — Official NVIDIA walkthrough.
• Building Agentic AI with NIM and LangGraph — End-to-end agent tutorial.
• RAG in 5 Minutes with NIM — Fast RAG setup guide.
• NVIDIA NIM: The Inference Layer for Agentic AI — NVIDIA blog on the NIM + LangChain integration.

Community & Discussion

• NVIDIA Developer Forums — NIM — Official support and discussion.
• r/NVIDIA — Reddit community.

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Contributing

Contributions welcome! Read the contribution guidelines first.

To add a resource:

1. Fork this repo.
2. Add your link in the relevant section — name, URL, and a one-sentence description.
3. Open a pull request.

Quality bar:

• NVIDIA NIM must be the primary or required inference/embedding backend — not one of many interchangeable LLM providers a project happens to support.
• Publicly accessible and actively maintained (last commit < 1 year, with rare exceptions for official NVIDIA reference repos).
• Real README, working setup instructions, and some evidence of actual use (stars, forks, or recent commits).
• No duplicates. No self-promotion without a working, documented project.

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Maintained by Daniel Deshmukh · Mumbai, India