Blog
The latest from OpenRelay — distributed GPU architecture, engineering deep dives, and what we're building.
Latest posts
How One Team Cut $4,000/mo in Vercel Build Costs with OpenRelay Runners
Replacing Vercel's build infrastructure with OpenRelay self-hosted GitHub runners saved $4,000/month in build minutes — with faster builds and zero config overhead.
Next-Gen GPUs Explained: H200, GB200, B200, MI300X for AI Inference
A complete guide to NVIDIA H200, GB200 NVL72, B200, and AMD MI300X GPUs. Specs, pricing, availability, and when each GPU makes sense for your AI workloads.
The Environmental Case for Distributed GPU Computing
Why reusing existing consumer GPUs for AI inference is greener than building new data centers. The environmental argument for distributed networks.
Kimi K2.5: The Open-Source Model That's Beating GPT-5.2 — And How to Host It
Moonshot AI's Kimi K2.5 is a 1T parameter open-source model outperforming closed-source giants on key benchmarks. Everything you need to deploy it on your own GPUs.
Best GPU Cloud for LLM Inference in 2026: Complete Guide
Compare the top GPU cloud providers for LLM inference. Side-by-side analysis of OpenRelay, RunPod, Vast.ai, Lambda, AWS, and GCP for models from 7B to 70B parameters.
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.
RunPod vs Lambda vs OpenRelay: GPU Cloud Comparison
Head-to-head comparison of three popular GPU cloud providers for AI inference workloads.
GPU Layers Explained: Optimizing Model Loading
Understanding GPU layers and how to optimize model loading for inference performance.
Running Stable Diffusion at Scale
How to deploy and scale Stable Diffusion for production image generation workloads.
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.
GPU-Accelerated GitHub Actions Runners
How to set up self-hosted GitHub Actions runners with GPU access for CI/CD pipelines.
Deploy Your First Model on OpenRelay
Step-by-step guide to deploying your first AI model on OpenRelay's GPU inference platform.
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.
How to Make Money from Your Gaming GPU
Turn your idle RTX 4090 or 3090 into a passive income stream. Rent out your GPU for AI inference and earn while you sleep.
GPU Cloud Pricing Comparison 2025: OpenRelay vs AWS vs GCP vs RunPod
Side-by-side comparison of GPU cloud pricing for ML inference. See how OpenRelay saves you 50-80% compared to AWS, Google Cloud, and other providers.
Ready to try it yourself?
Deploy your first fault-tolerant inference cluster in minutes. No credit card required.
Get started free