OpenCode System with NVIDIA Models

Building AI‑powered platforms that go beyond basic code assistance — delivering real‑time, context‑aware, highly optimized, secure, and scalable code intelligence. By leveraging NVIDIA's GPU‑accelerated AI stack, you can unlock unprecedented performance for open‑source code tasks (generation, optimization, analysis, security, etc.).

📁 Project Structure

opencode-nvidia/
├── models/                 # Model configurations & conversion scripts
├── configs/                # Configuration files
├── triton_server/          # Triton Inference Server setup
├── backend/                # FastAPI backend service
├── plugins/vscode/         # VS Code extension
├── scripts/                # Utility scripts
├── docker-compose.yml      # Container orchestration
└── README.md               # This file

🚀 Core Principles

1. Real‑time, low‑latency inference (sub‑100 ms for suggestions)
2. Context‑aware understanding of entire repositories
3. Multi‑modal intelligence (code + comments + docs + diagrams)
4. Self‑optimizing & self‑securing (auto‑refactor, auto‑patch vulnerabilities)
5. Scalable across clusters (thousands of GPUs)
6. Open‑source first – all components are open, extensible, and community‑driven

🛠️ NVIDIA Stack Components

Component	Purpose
CUDA / cuDNN	GPU acceleration foundation
TensorRT / TensorRT‑LLM	Ultra‑fast inference (FP16/BF16/FP8)
NVIDIA Triton Inference Server	Dynamic batching, multi‑model ensembles
NCCL	Multi‑GPU / multi‑node communication
NVIDIA NeMo	Fine‑tune LLMs on code corpus
cuML / cuGraph	GPU‑accelerated code‑graph analysis
NGC Registry	Pre‑optimized AI models
DeepSpeed	Efficient training/fine‑tuning

📋 Quick Start

Prerequisites

• NVIDIA GPU (A100/H100 recommended)
• Docker with NVIDIA Container Toolkit
• Kubernetes (for production deployment)

Step 1: Pull Pre-optimized Model

docker run -it --gpus all \
  -p 8000:8000 \
  nvcr.io/nim/meta/codegen-starmath-1.0

Step 2: Test the API

curl -X POST http://localhost:8000/generate \
     -H "Content-Type: application/json" \
     -d '{
           "prompt": "# Write a CUDA kernel to add two vectors\ndef vector_add_cuda(",
           "max_tokens": 200
         }'

Step 3: Deploy Full Stack

docker-compose up -d

🏗️ Architecture

┌─────────────┐     ┌───────────────────┐     ┌──────────────┐
│   Clients   │────▶│  Triton Server(s)  │◀────│  GPU Nodes   │
│ (IDE/CLI)   │     │  (Model Routing)   │     │ (A100/H100)  │
└─────────────┘     └───────────────────┘     └──────────────┘
                               │
                               ▼
                      ┌───────────────────────┐
                      │  Vector DB (Milvus)   │
                      │  + cuGraph Engine     │
                      └───────────────────────┘

📖 Documentation

• Model Optimization Guide
• Triton Server Setup
• VS Code Plugin Development
• Security & Compliance

🤝 Contributing

This is an open-source project. Tag your contributions with #OpenCode-NVIDIA!

📄 License

MIT License - See LICENSE for details.