rbee (pronounced "are-bee")

Your private AI cloud, in one command.

rbee is a system that turns every GPU you own—across desktops, laptops, servers, and remote machines—into a single, unified AI cloud with one API and one interface.

Mac, PC, Linux—all working together. Zero ongoing costs. Zero vendor lock-in. One OpenAI-compatible API.

🎯 Finally, the correct way to run AI across all your GPUs.

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The Problem

You have multiple GPUs across different machines. Maybe a gaming PC with an RTX 4090, a Mac Studio with M2 Ultra, an old server with dual RTX 3090s.

Right now, you're probably:

• Juggling multiple tools with different ports and configurations
• Manually deciding which model runs where
• Dealing with GPU memory conflicts and crashes
• Leaving GPUs idle while others are overloaded
• SSH'ing into different machines to manage everything

Or you're paying for cloud APIs when you already own the hardware.

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The Solution

Of course you should use all your GPUs. Why wouldn't you?

rbee makes it obvious:

# Single command. Zero configuration. Just works.
rbee infer -m llama-3-8b -p "Generate a React component"

# Behind the scenes:
# ✓ Queen starts automatically
# ✓ Worker spawns on best GPU
# ✓ Inference runs
# ✓ Zero manual setup

# Multi-machine? Same simplicity:
rbee hive install gaming-pc     # Your RTX 4090
rbee hive install mac-studio    # Your M2 Ultra
rbee infer -m llama-3-70b -p "Refactor this function"

# rbee orchestrates everything automatically

Make your IDE use YOUR infrastructure:

# Point Zed/Cursor/Continue.dev to rbee
export OPENAI_API_BASE=http://localhost:7833/v1
export OPENAI_API_KEY=your-rbee-token

# Now your AI tooling runs on YOUR GPUs
# Models never change without permission
# Zero ongoing costs (electricity only)
# Complete control

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Why rbee?

All Your Machines, One System

Your gaming PC with RTX 4090. Your Mac Studio with M2 Ultra. Your old server with 2x RTX 3090. All working together as one unified system.

• Chat with an LLM and generate images at the same time
• No manual port juggling
• No SSH'ing into chaos
• One OpenAI-compatible endpoint for everything

Zero Kubernetes. SSH In, Hive Up.

No cluster PhD required. No complex orchestration. SSH-based deployment that just works.

# Add a machine like SSH config
rbee hive install gaming-pc

# Use it
rbee infer -a gaming-pc -m llama-3-70b -p "Complex task"

Intelligent Scheduling

Like a bee colony: one queen coordinates, multiple workers execute. Your GPUs work at optimal capacity. No wasted compute. No idle resources.

Same hardware. Smarter scheduling. 40-60% better GPU utilization.

Stop Paying for Cloud APIs

• Cloud APIs: $20-100/month = $240-1,200/year
• rbee: Electricity only (~$10-30/month)
• Your hardware. Your control. Zero ongoing costs.

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Up and Running in 5 Minutes

No complex configuration. No Kubernetes. No payment information. Just download and start orchestrating.

Step 1: Install rbee

# One command gets you started
cargo install rbee-keeper

# Or download from GitHub releases
# Works on Mac, PC, Linux

Step 2: Use It

# Localhost automatically detected (zero config!)
rbee infer -m llama-3-8b -p "Hello world"

# Multi-machine? Add machines like SSH config:
cat ~/.config/rbee/hives.conf
# Host gaming-pc
#   HostName 192.168.1.100
#   User vince
#   HivePort 7835

# Install remotely via SSH
rbee hive install gaming-pc

# Use it
rbee infer -a gaming-pc -m llama-3-70b -p "Complex task"

Step 3: Point Your IDE to rbee

export OPENAI_API_BASE=http://localhost:7833/v1
export OPENAI_API_KEY=your-token

# Zed, Cursor, Continue.dev—all work
# Your existing code runs unchanged
# Drop-in replacement for OpenAI API

That's it. 5 minutes. Your GPUs. Your AI infrastructure.

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What is rbee (Technical)?

rbee is an OpenAI-compatible GPU orchestration platform with three core binaries:

1. rbee-keeper — Your Interface (CLI)

Port: N/A (thin HTTP client)
You interact with this

• Manages queen lifecycle (auto-start, auto-update)
• SSH-based hive installation (like Ansible)
• Worker/model/inference commands
• Command: rbee

2. queen-rbee — The Brain (Daemon)

Port: 7833 (default)
Makes ALL intelligent decisions

• Job-based architecture (everything is a job)
• Routes operations to hives (via HTTP)
• Inference scheduling & worker registry
• Can embed hive logic for localhost (zero config)

3. rbee-hive — Worker Manager (Daemon)

Port: 7835 (default, configurable)
Runs ON GPU machines

• Installed via SSH
• Manages workers on THAT machine only
• Model catalog (SQLite) + backend detection
• One hive per GPU machine

Worker Variants (Extensible)

• llm-worker-rbee — llama.cpp-based inference ✅ M0 DONE
• Future: CUDA-optimized, Metal-optimized, vLLM adapter, ComfyUI adapter

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The Architecture: Queen & Workers

Think of it like a bee colony:

Your Command (You)
    ↓
rbee CLI (Thin Client)
    ↓ HTTP to queen
Queen Bee (Brain - Port 7833)
    ↓ Routes to hives
Hive Managers (Port 7835 each)
    ↓ Spawns workers
Worker Bees (Execute on GPUs)

Single Machine (Integrated):

rbee CLI → Queen (embedded hive) → Workers

• Zero configuration
• Auto-starts queen
• Just works

Multiple Machines (Distributed):

rbee CLI → Queen → Hive (gaming-pc) → Workers
                → Hive (mac-studio) → Workers
                → Hive (old-server) → Workers

• SSH-based installation
• Automatic orchestration
• Bees working together

Why This Architecture?

Smart/Dumb Separation:

• Queen = Brain (makes decisions)
• Hives = Managers (lifecycle on each machine)
• Workers = Muscle (execute ONE model each)

Benefits:

• Queen can run without GPUs (routes to remote workers)
• Workers are stateless (kill anytime, no data loss)
• Clean testable components
• Multi-architecture support (CUDA, Metal, CPU)
• Process isolation (workers own their memory)

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How rbee Compares

vs. Ollama / LM Studio

Ollama and LM Studio are excellent single-machine tools. They're simple, mature, and battle-tested.

rbee is for multiple machines:

• Multi-machine orchestration (use ALL your GPUs)
• Heterogeneous hardware (CUDA + Metal + CPU together)
• Production-grade scheduling and routing
• OpenAI-compatible API across your entire fleet

Use Ollama if: You have one machine and want simplicity
Use rbee if: You have multiple GPUs across multiple machines

vs. Cloud APIs (OpenAI, Together.ai)

Cloud APIs are convenient. No infrastructure management, access to latest models immediately.

rbee gives you control:

• Your models, your rules (never change without permission)
• Your data stays on your network (complete privacy)
• Zero ongoing costs (electricity only)
• Your hardware, your uptime

Use cloud APIs if: You want zero infrastructure management
Use rbee if: You already own GPUs and want independence

vs. Kubernetes-based Stacks

Kubernetes is powerful. It can orchestrate anything.

rbee is focused:

• SSH-based deployment (no cluster complexity)
• Built specifically for AI workloads
• Works on home networks, not just data centers
• Zero Kubernetes knowledge required

Use Kubernetes if: You're running a data center
Use rbee if: You want AI orchestration without the PhD

What Only rbee Does

• Multi-machine + multi-device: Mac + PC + Linux working as one system
• SSH-based: No Kubernetes, no complex setup
• Heterogeneous hardware: CUDA, Metal, CPU in one unified system
• Home network optimized: Built for your hardware, not cloud data centers
• OpenAI-compatible: Drop-in replacement for your existing tools

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Current Status

Version: 0.1.0 (M0 - Core Orchestration)
Completion: 95% (in progress)
License: GPL-3.0-or-later (free and open source, copyleft)

What's Working Now (M0 - Complete)

• ✅ Multi-machine orchestration (SSH-based)
• ✅ Auto-start queen with auto-update
• ✅ Worker lifecycle management
• ✅ Model downloading (HuggingFace)
• ✅ OpenAI-compatible API
• ✅ SSE token streaming
• ✅ Basic inference (llama.cpp backend)
• ✅ Heterogeneous backends (CUDA, Metal, CPU detection)

What's Coming Next

M1 (Q1 2026) - Production-Ready:

• Improved error handling
• Better GPU selection
• Performance optimizations
• Documentation polish

M2 (Q2 2026) - Advanced Features:

• Rhai scheduler (custom routing scripts)
• Web UI dashboard
• Advanced telemetry
• Multi-user support

M3 (Q3 2026) - Multi-Modal:

• Image generation (Stable Diffusion)
• Audio transcription (Whisper)
• Video processing
• Multi-modal workflows

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Quick Start Examples

Use Case 1: AI-Assisted Development

# Configure Zed IDE to use rbee
export OPENAI_API_BASE=http://localhost:7833/v1
export OPENAI_API_KEY=your-rbee-token

# Now Zed's AI assistant uses your GPUs
# While you generate UI mockups with another model
# Both at the same time, zero conflicts

Use Case 2: Content Creation Workflow

# 1. Generate script with LLM (Mac M2 Ultra)
rbee infer -a mac-studio -m llama-3-70b -p "Write a video script about..."

# 2. Generate images for video (RTX 4090)
rbee infer -a gaming-pc -m sdxl -p "Professional product photo of..."

# 3. All running in parallel across your GPU farm

Use Case 3: Test Multiple LLMs in Parallel

# Run A/B tests across different models
rbee infer -m llama-3-8b -p "Explain quantum computing"
rbee infer -m llama-3-70b -p "Explain quantum computing"
rbee infer -m mistral-7b -p "Explain quantum computing"

# Compare outputs
# Choose best model for your use case

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Technical Specifications

Supported Platforms

• Linux - NVIDIA GPUs (CUDA), AMD GPUs (ROCm), CPU fallback
• macOS - Apple Silicon (Metal), Intel Macs (CPU)
• Windows - NVIDIA GPUs (CUDA), CPU fallback

Backend Support

• ✅ llama.cpp (CUDA, Metal, CPU)
• 🚧 vLLM (planned)
• 🚧 Stable Diffusion (planned)
• 🚧 Whisper (planned)

API Compatibility

• OpenAI Chat Completions API
• Server-Sent Events (SSE) streaming
• Token-by-token generation
• Temperature control (0.0-2.0)
• Seed support (for testing)

System Requirements

• Minimum: 8GB RAM, 4GB VRAM (or Metal unified memory)
• Recommended: 16GB+ RAM, 8GB+ VRAM per worker
• Network: SSH access for multi-machine setup

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Installation

Quick Install

# Install from source
cargo install rbee-keeper

# Or download binary from GitHub releases
# https://github.com/rbee-keeper/rbee/releases

Build from Source

# Clone repository
git clone https://github.com/rbee-keeper/rbee.git
cd llama-orch

# Build all binaries
cargo build --release

# Binaries in target/release/:
# - rbee-keeper (CLI)
# - queen-rbee (orchestrator)
# - rbee-hive (worker manager)
# - llm-worker-rbee (inference)

First Run

# Zero configuration needed for localhost
rbee infer -m llama-3-8b -p "Hello, rbee!"

# Downloads model automatically
# Starts queen automatically
# Spawns worker automatically
# Just works

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Configuration

Hives Configuration

# ~/.config/rbee/hives.conf (like ~/.ssh/config)
Host gaming-pc
  HostName 192.168.1.100
  User vince
  HivePort 7835

Host mac-studio
  HostName 192.168.1.101
  User vince
  HivePort 7835

Host old-server
  HostName 192.168.1.102
  User vince
  HivePort 7835

Remote Installation

# Install hive on remote machine (via SSH)
rbee hive install gaming-pc

# Copies binary, starts daemon, configures systemd
# One command, fully automatic

IDE Configuration

# Zed IDE
export OPENAI_API_BASE=http://localhost:7833/v1
export OPENAI_API_KEY=your-rbee-token

# Cursor IDE
# Settings → Features → OpenAI API Base URL
# http://localhost:7833/v1

# Continue.dev
# config.json:
{
  "models": [{
    "provider": "openai",
    "apiBase": "http://localhost:7833/v1",
    "model": "llama-3-8b"
  }]
}

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Community & Support

Early stage project - Solo developer actively building

• GitHub: rbee-keeper/rbee
• Status: Active development (v0.1.0, 68% complete)
• License: GPL-3.0-or-later (free forever)

Get Help:

• GitHub Discussions - Ask questions
• GitHub Issues - Report bugs
• Documentation - Detailed guides
• Architecture Docs - Technical deep dives

Contribute:

• Read CONTRIBUTING.md
• Check Engineering Rules
• See Good First Issues

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Project Philosophy

Built with AI, For AI

This project is 99% AI-generated code via Character-Driven Development. Six specialized AI teams work together to build rbee. This proves: a good architect can vibe code beyond their normal capabilities.

Why GPL-3.0?

Copyleft protects user freedom. If you modify rbee and distribute it, you must share your improvements. This keeps AI infrastructure free and open for everyone.

Why "rbee"?

r = Rust + bee = distributed workers. Like a bee colony: one queen (brain) coordinates multiple worker bees (GPUs). Each bee knows its role. Together, they accomplish what no single bee can.

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Roadmap

M0 - Core Orchestration (Complete ✅)

• Multi-machine orchestration
• Worker lifecycle management
• Basic inference
• OpenAI-compatible API

M1 - Production-Ready (Q1 2026)

• Error handling improvements
• GPU selection refinements
• Performance optimizations
• Documentation polish

M2 - Advanced Features (Q2 2026)

• Rhai scheduler (custom routing scripts)
• Web UI dashboard
• Advanced telemetry
• Multi-user support

M3 - Multi-Modal (Q3 2026)

• Image generation (Stable Diffusion)
• Audio transcription (Whisper)
• Video processing
• Multi-modal workflows

M4 - Enterprise Features (Q4 2026)

• Multi-tenancy
• GDPR compliance module
• Advanced analytics
• SLA monitoring

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Performance

Latency

• Job creation: <1ms
• SSE connection: <10ms
• First token: <100ms (model-dependent)
• Subsequent tokens: <10ms (model-dependent)

Throughput

• Single worker: Up to model's native speed
• Multiple workers: Linear scaling (N workers = N× throughput)
• Overhead: <5% (HTTP + serialization)

Resource Usage

• Queen: ~50MB RAM (idle), ~100MB (active)
• Hive: ~30MB RAM per hive
• Worker: Model size + ~500MB overhead
• Total: Efficient, scales with workload

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Security

CRITICAL: This is 99% AI-generated code. DO NOT use in production without security audit.

• Read SECURITY.md for vulnerability reporting
• Five security crates (auth-min, audit-logging, input-validation, secrets-management, deadline-propagation)
• GDPR compliance built-in (7-year audit retention)
• Process isolation (workers run separately)

We take security seriously, but you must audit before production use.

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License

GPL-3.0-or-later - Free and open source, copyleft

You can:

• ✅ Use rbee for personal projects
• ✅ Use rbee for commercial projects
• ✅ Modify rbee
• ✅ Distribute rbee

You must:

• ✅ Share source code if you distribute modifications
• ✅ Keep GPL-3.0 license
• ✅ Credit original authors

See LICENSE for full terms.

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About the Project

Built by: Vince Liem
Development approach: 99% AI-generated code via Character-Driven Development

rbee was created because I needed a simple way to run multiple AI workloads across multiple machines—without Kubernetes, hacks, or guesswork. The architecture that solved my own setup scaled naturally into something anyone with GPUs can use.

I take ownership of:

• ✅ High-level architecture
• ✅ Crate structure and code flow
• ✅ Language choices and specifications

I take FULL responsibility for:

• ❌ Breaking bugs (I will fix them)
• ❌ Security failures (I will patch them)

Honest disclaimer: This is 99% AI-generated code. You should audit before production use. I encourage audits and welcome human code reviewers.

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Acknowledgments

Built with:

• Windsurf - AI-powered IDE (not sponsored, but hey 👋)
• Rust + Tokio + Axum
• llama.cpp
• Character-Driven Development

Inspired by:

• Ollama (simplicity)
• vLLM (performance)
• OpenAI (API design)

Special thanks:

• All AI teams who generated 99% of this code
• Human reviewers (desperately needed)
• Early adopters and testers

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Get Started Now

# 1. Install
cargo install rbee-keeper

# 2. Run
rbee infer -m llama-3-8b -p "Hello, rbee!"

# 3. Point your IDE to rbee
export OPENAI_API_BASE=http://localhost:7833/v1

# 4. Build with AI. Own your infrastructure.

Stop juggling tools. Stop SSH'ing into chaos. rbee is the system you needed.

🐝 rbee - All your GPUs, one system.