Pick the model.
Stand up the endpoint.
The top 25 open-weight models on OpenRouter, mapped to the GPU clouds that can actually run them. Every cell opens a provider-specific runbook with hardware, runtime, source docs, smoke tests, and an agent-ready prompt.
Choose any model × provider pair
Green paths are single-GPU. Purple paths share one multi-GPU host. Amber paths require multiple connected nodes. Red paths deliberately withhold commands when the evidence is incomplete.
Seven clouds, seven different setup surfaces
The shortest UI path from a Hugging Face ID to an OpenAI-compatible endpoint.
Serverless or a dedicated Pod on one node; Instant Clusters across nodes →Cost-sensitive dedicated containers when you can evaluate marketplace hosts yourself.
A marketplace Docker instance on one host; physical clusters and overlays across hosts →Straightforward dedicated NVIDIA instances and full multi-GPU nodes.
On-Demand Cloud on one node; a reserved 1-Click Cluster across nodes →Production teams that need VPC controls, durable model storage, and multi-node operations.
SageMaker HyperPod inference, from one worker node to a multi-node EKS deployment →Teams standardizing model serving in Vertex AI with custom containers and raw prediction.
Vertex AI on one node; GKE Standard for one model spanning multiple nodes →Enterprise deployments that need managed endpoints, identities, rollouts, and Azure controls.
Azure ML managed online deployment on one node; AKS across nodes →Bare-metal GPU shapes and OCI-native imported-model or Kubernetes deployments.
OCI imported models when compatible; OKE for custom single- or multi-node serving →Hand this runbook to Claude Code or Codex
Open a terminal in the repository where you want the deployment files,
start claude or codex, then paste this prompt.
It asks the agent to verify sources and stop before it creates billable infrastructure.
Use the infrastructure or model context on this page to create a reproducible open-model deployment. Use this guide as the starting context: https://www.getflops.ai/. Read the linked model card and provider documentation before choosing hardware or runtime settings. Open every linked primary source and flag any mismatch instead of guessing. Create a deployment folder containing README.md, .env.example with no secrets, a pinned start script or infrastructure manifest, and smoke-test.sh. Make the endpoint OpenAI-compatible where the runtime supports it. Run local/static validation, estimate the billable resources, and stop before provisioning paid infrastructure until I approve.