How does rbee reduce environmental impact?

rbee uses local-first AI orchestration, eliminating unnecessary data center traffic. By keeping computation on your hardware, we reduce energy consumption and carbon footprint compared to cloud-based AI services.

What is local-first AI?

Local-first AI means running AI models on your own hardware instead of sending data to cloud servers. This reduces network overhead, data center energy consumption, and latency while improving privacy.

How much energy can I save with rbee?

Self-hosted AI with rbee uses only the power your hardware consumes. Cloud APIs require data center infrastructure, cooling, and redundancy. For typical use cases, self-hosting saves 60-80% energy compared to cloud APIs.

🌱 Environmental Sustainability

Building Greener AI Infrastructure

Local-first AI reduces data center overhead, optimizes GPU utilization, and minimizes energy waste. Every idle GPU we activate is one less GPU that needs to be manufactured.

View on GitHub Read Our Blog

Why Local-First AI Can Be Greener

No Data Center Overhead

Scenario

Cloud AI requires massive data centers with cooling, redundancy, and 24/7 operations.

Solution

rbee runs on hardware you already own—offices, homelabs, workstations.

Outcome

Zero additional infrastructure. Zero additional cooling. Zero additional construction.

Reduced Network Traffic

Scenario

Every API call to the cloud travels hundreds or thousands of miles.

Solution

Local inference means data stays on your network.

Outcome

Lower latency, lower bandwidth, lower carbon footprint from network infrastructure.

Activate Idle Hardware

Scenario

Millions of GPUs sit idle in gaming PCs, workstations, and servers.

Solution

rbee orchestrates existing hardware instead of requiring new purchases.

Outcome

Maximize utilization of existing resources. Reduce e-waste and manufacturing demand.

Right-Sized Workloads

Scenario

Cloud providers over-provision for peak loads, wasting energy during off-peak hours.

Solution

rbee packs workloads efficiently across heterogeneous hardware.

Outcome

Use the right GPU for the right task. No more RTX 4090 for simple chat completions.

What rbee Does Specifically

Multi-Machine Orchestration

Distribute workloads across all available GPUs. Fewer machines sit idle. Higher utilization means fewer GPUs need to be manufactured.

No Forced Cloud Dependency

Run entirely on your own infrastructure. No data center overhead. No vendor lock-in. No hidden energy costs.

Efficient Workload Packing

Pack multiple workloads onto limited hardware through intelligent scheduling. Reduce the total number of machines needed.

Heterogeneous Hardware Support

Mix NVIDIA, Apple, AMD, and CPU workers. Use what you have instead of buying new hardware.

Current Practices & Future Plans

Today: Open-Source Infrastructure

Scenario

Transparent code means community audits and contributions.

Solution

GPL-3.0 licensed core. No proprietary black boxes.

Outcome

Efficient algorithms developed in public. Community-driven optimization.

Today: Efficient Scheduling

Scenario

Intelligent routing minimizes idle time and maximizes GPU utilization.

Solution

Rhai-based scheduling with real-time metrics.

Outcome

Higher utilization = fewer GPUs needed = lower environmental impact.

Future: Power-Aware Scheduling

Scenario

Not all GPUs are equal in power consumption.

Solution

Route workloads to the most energy-efficient hardware available.

Outcome

Prioritize efficient GPUs. Reduce total power draw.

Future: Renewable Energy Integration

Scenario

Solar panels generate peak power during the day.

Solution

Schedule non-urgent jobs during renewable energy availability.

Outcome

Align compute with clean energy. Reduce fossil fuel dependency.

How to Run rbee Sustainably

Schedule Off-Peak Jobs

Run non-urgent workloads during off-peak hours when renewable energy is more available and grid demand is lower.

Monitor GPU Utilization

Track which GPUs are idle. Consolidate workloads onto fewer machines. Power down unused hardware.

Match Workload to Hardware

Use heterogeneous hardware strategically. Don't waste a 4090 on simple tasks. Route efficiently.

Enable Power Management

Configure idle workers to enter low-power states. Reduce energy consumption when not actively processing.

Consolidate Workloads

Pack multiple workloads onto fewer machines when possible. Reduce total power draw and cooling requirements.

Start Building Sustainably

Turn Your Idle GPUs Into a Greener AI Infrastructure

This page is linked from our LinkedIn Environmental Sustainability commitment.

Download rbee Read Documentation

📄 Referenced in our LinkedIn Environmental Sustainability Report

🌱 Environmental Sustainability

Building Greener AI Infrastructure

Local-first AI reduces data center overhead, optimizes GPU utilization, and minimizes energy waste. Every idle GPU we activate is one less GPU that needs to be manufactured.

View on GitHub Read Our Blog

Why Local-First AI Can Be Greener

No Data Center Overhead

Scenario

Cloud AI requires massive data centers with cooling, redundancy, and 24/7 operations.

Solution

rbee runs on hardware you already own—offices, homelabs, workstations.

Outcome

Zero additional infrastructure. Zero additional cooling. Zero additional construction.

Reduced Network Traffic

Scenario

Every API call to the cloud travels hundreds or thousands of miles.

Solution

Local inference means data stays on your network.

Outcome

Lower latency, lower bandwidth, lower carbon footprint from network infrastructure.

Activate Idle Hardware

Scenario

Millions of GPUs sit idle in gaming PCs, workstations, and servers.

Solution

rbee orchestrates existing hardware instead of requiring new purchases.

Outcome

Maximize utilization of existing resources. Reduce e-waste and manufacturing demand.

Right-Sized Workloads

Scenario

Cloud providers over-provision for peak loads, wasting energy during off-peak hours.

Solution

rbee packs workloads efficiently across heterogeneous hardware.

Outcome

Use the right GPU for the right task. No more RTX 4090 for simple chat completions.

What rbee Does Specifically

Multi-Machine Orchestration

Distribute workloads across all available GPUs. Fewer machines sit idle. Higher utilization means fewer GPUs need to be manufactured.

No Forced Cloud Dependency

Run entirely on your own infrastructure. No data center overhead. No vendor lock-in. No hidden energy costs.

Efficient Workload Packing

Pack multiple workloads onto limited hardware through intelligent scheduling. Reduce the total number of machines needed.

Heterogeneous Hardware Support

Mix NVIDIA, Apple, AMD, and CPU workers. Use what you have instead of buying new hardware.

Current Practices & Future Plans

Today: Open-Source Infrastructure

Scenario

Transparent code means community audits and contributions.

Solution

GPL-3.0 licensed core. No proprietary black boxes.

Outcome

Efficient algorithms developed in public. Community-driven optimization.

Today: Efficient Scheduling

Scenario

Intelligent routing minimizes idle time and maximizes GPU utilization.

Solution

Rhai-based scheduling with real-time metrics.

Outcome

Higher utilization = fewer GPUs needed = lower environmental impact.

Future: Power-Aware Scheduling

Scenario

Not all GPUs are equal in power consumption.

Solution

Route workloads to the most energy-efficient hardware available.

Outcome

Prioritize efficient GPUs. Reduce total power draw.

Future: Renewable Energy Integration

Scenario

Solar panels generate peak power during the day.

Solution

Schedule non-urgent jobs during renewable energy availability.

Outcome

Align compute with clean energy. Reduce fossil fuel dependency.

How to Run rbee Sustainably

Schedule Off-Peak Jobs

Run non-urgent workloads during off-peak hours when renewable energy is more available and grid demand is lower.

Monitor GPU Utilization

Track which GPUs are idle. Consolidate workloads onto fewer machines. Power down unused hardware.

Match Workload to Hardware

Use heterogeneous hardware strategically. Don't waste a 4090 on simple tasks. Route efficiently.

Enable Power Management

Configure idle workers to enter low-power states. Reduce energy consumption when not actively processing.

Consolidate Workloads

Pack multiple workloads onto fewer machines when possible. Reduce total power draw and cooling requirements.