Blog
The latest from OpenRelay — distributed GPU architecture, engineering deep dives, and what we're building.
How to Reduce LLM Inference Costs by 80% in 2026
Practical strategies to cut your GPU inference bill — from right-sizing GPUs and quantization to distributed inference on consumer hardware.
Distributed GPU Inference Explained: How Overlay Networks Power Fault-Tolerant AI
How distributed GPU inference works, why overlay networks enable automatic failover, and how OpenRelay built a fault-tolerant inference platform on consumer hardware.
GPU Layers Explained: Optimizing Model Loading
Understanding GPU layers and how to optimize model loading for inference performance.
Real-Time AI Inference: Architecture and Best Practices
Building low-latency AI inference pipelines for real-time applications.
LLM Inference at Scale: Lessons Learned
Practical lessons from scaling LLM inference to thousands of concurrent users.
How OpenRelay Works: The Big Picture
An overview of OpenRelay's architecture — a distributed GPU overlay network that automatically routes around failures. Part one of a three-part series.
Why We Keep Container Deployments Simple (And You Should Too)
OpenRelay deliberately chose a simple 'one container per cluster' model over complex multi-container orchestration. That's a feature, not a limitation.
The Agent: Node Software, Heartbeats, and Container Management
How the agent runs on GPU nodes, manages dependencies, reports health, and executes container deployments with VM-level isolation.
Fault Tolerance: Health Checks, Failover, and Self-Healing
How OpenRelay detects failures, routes around unhealthy nodes, and automatically recovers workloads without manual intervention.
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