Provider deployment desk
Deploy open models
on Lambda.
Straightforward dedicated NVIDIA instances and full multi-GPU nodes.
25 model runbooks
Start with deployability, then compare quality
The rank is observed demand. The status is the hardware and orchestration burden on Lambda.
Agent-assisted setup
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.
Deployment prompt
Prepare a reproducible open-model deployment project for Lambda; ask me to choose one of the models on this page before selecting GPU capacity. Use this guide as the starting context: https://www.getflops.ai/models/provider/lambda. 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.
Guardrails included
No secrets in files · verify primary docs · approval before spend
Provider source