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RAZER AIKIT

🚀 Quick Start • 📖 Documentation • 🛠️ Contributing

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⚠️ Preview Release

Razer AIKit is in preview. Features may change or have limitations before stable release. We appreciate your feedback!

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What is Razer AIKit?

Open-source AI development environment for engineers and researchers. Delivers cloud-grade performance and scalability directly on your desktop with out-of-the-box setup.

Technical Stack

• AIKit CLI - Command-line interface for model lifecycle management
• vLLM Engine - Production-grade LLM inference with memory optimization
• LlamaFactory - Parameter-efficient fine-tuning framework
• Ray - Distributed computing for seamless multi-GPU scaling

Core Features

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🎨 AI Image Generation

Run iterative image generation locally - craft creative prompts and refine results in real time with zero cloud dependency.

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🎯 Production-Ready Text Generation

Instantly test any model through our built-in Open WebUI - select, prompt, and iterate without any extra setup.

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🌐 Intelligent Multi-GPU Scaling

Monitor live GPU load in Grafana and seamlessly scale from one overloaded GPU to a full multi-GPU cluster.

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🚀 Local-First AI Development

Explore 300,000+ models on-device - launch AIKit, open Jupyter Lab, and follow step-by-step notebook guides to run your first model locally.

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Quick Start

Prerequisites

<details> <summary><b>Windows 11</b></summary>

Note: Razer AIKit runs inside WSL 2 on Windows for optimal performance and compatibility.

• NVIDIA GPU Driver - Install NVIDIA App and select Studio Driver for best stability

• WSL 2 - Microsoft's guide

  • Install 24.04 distribution
  • Configure networking mode to Mirrored in WSL Settings
  • Configure Windows Firewall for WSL: Run the following command in PowerShell as Administrator to allow incoming connections to WSL:

    Set-NetFirewallHyperVVMSetting -Name '{40E0AC32-46A5-438A-A0B2-2B479E8F2E90}' -DefaultInboundAction Allow
    

• Docker Engine (WSL) - Install guide

• NVIDIA Container Toolkit (WSL) - Installation instructions

Verify installation:

nvidia-smi

</details> <details> <summary><b>Ubuntu 22.04 / 24.04</b></summary>

• Docker Engine - Install guide
• NVIDIA GPU Driver - Install via Software & Updates > Additional Drivers
• NVIDIA Container Toolkit - Installation instructions

Verify installation:

nvidia-smi

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⚡ Quick Start & Your First Model

# Create huggingface cache directory if it doesn't exist
mkdir -p $HOME/.cache/huggingface

# Pull and run Razer AIKIT
docker run -it \
  --restart=unless-stopped \
  --gpus all \
  --ipc host \
  --network host \
  --mount type=bind,source=$HOME/.cache/huggingface,target=/home/Razer/.cache/huggingface \
  --env HUGGING_FACE_HUB_TOKEN=<YOUR_TOKEN> \
  razerofficial/aikit:latest

Once inside the container, choose your preferred approach:

Option 1: Start with interactive guides

# Start Jupyter Lab for interactive guides (guides will be available at the outputted link)
jupyter lab --ip="0.0.0.0"

💡 Tip: Explore the notebooks/ folder for step-by-step guides and examples!

OR

Option 2: Run a model directly

# Run a lightweight coding model
rzr-aikit model run deepseek-ai/deepseek-coder-1.3b-instruct

🔬 Advanced Mode

For production deployments and monitoring, enable the full stack with Docker Compose:

git clone https://github.com/razerofficial/aikit.git && cd aikit
mkdir -p $HOME/.cache/huggingface
export HUGGING_FACE_HUB_TOKEN=<YOUR_TOKEN>
docker compose -f docker_compose/docker-compose.yaml up -d --pull always

Available Services:

• 📓 Jupyter Lab - Interactive notebooks with examples (http://localhost:8888)
• 📊 Grafana - GPU metrics, model performance, Ray cluster stats (http://localhost:3000)
• 💬 Open WebUI - Chat interface for model testing (http://localhost:1919)
• 🎯 Prometheus - Metrics collection (http://localhost:9090)

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📚 Documentation & Examples

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📖 Documentation

• CLI Reference - Complete command reference
• Setup Guide - Detailed installation instructions
• GPU Compatibility - Supported GPUs and compute capabilities
• Fine-Tuning Guide - Model customization
• Inference Guide - Production deployment
• Container Guide - Docker setup
• Known Issues - Common problems and solutions

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💡 Interactive Examples

• On-Device Inference
• Distributed Inference
• Fine-Tuning with LoRA
• OpenAI API Integration
• Semantic Search

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🖥️ Platform Support

Razer AIKit is optimized for NVIDIA accelerated computing platforms with support for both x86-64 and ARM64 architectures.

Workstations & Development

• Razer Blade - High-performance gaming laptops with GeForce RTX 50 series, RTX 40 series, RTX 30 series, and RTX 20 series
• NVIDIA RTX Professional Workstations - RTX PRO 6000 Blackwell, RTX 6000 Ada, RTX A series

Data Center & Enterprise

• NVIDIA DGX Systems (x86-64 and ARM64)
  • NVIDIA GB10 (DGX Spark)
  • NVIDIA GB300, GB200
  • NVIDIA GH200, GH100
• Data Center GPUs: B200, B300, H100, H200, A100, L4, L40, L40S

GPU Requirements

• Minimum: NVIDIA GPU with Compute Capability 7.0 (Volta) or higher
• Supported Architectures: Blackwell, Hopper, Ada Lovelace, Ampere, Turing, Volta
• Detailed Information: GPU Compatibility Guide

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🏆 Contributors

We welcome contributions from the community! Special thanks to all our contributors who make this project possible.

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📄 License & Acknowledgments

Licensed under Apache License 2.0

Additional Resources

• Security Policy - Security reporting guidelines
• Open Source Notices - Third-party acknowledgments
• Contributing Guidelines - Detailed contribution instructions

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Made with ❤️ by the Razer AI Team

🌟 Star us on GitHub

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