OpenRelay

How OpenRelay's kernel agents got 1.5x more traffic out of each AMD MI355X serving Gemma 4 31B, at the same latency target and the same accuracy. We are open-sourcing all of it.

orl 0.4 adds GPU pods, VM sizing checks before launch, per-VM burn rates, and expedited batch inference. Install it and rent a GPU from your terminal.

Two changes that cut most LLM pipeline bills 3-10x: right-size the model so a 20B screens the easy majority, and cache the shared prefix so repeated system prompts bill at one tenth the rate.

We're building the CDN of inference: a distributed GPU network that makes fast, affordable, fault-tolerant AI compute available to everyone. Here's why, and what comes next.

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.

Why reusing existing consumer GPUs for AI inference is greener than building new data centers. The environmental argument for distributed networks.

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.

How distributed GPU inference works, why overlay networks enable automatic failover, and how OpenRelay built a fault-tolerant inference platform on consumer hardware.

Practical strategies to cut your GPU inference bill: from right-sizing GPUs and quantization to distributed inference on consumer hardware.

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.