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MD on Runpod

Run the GPU molecular-dynamics service (gromacs-md) on a Runpod GPU pod, so run_molecular_dynamics works without you owning a card. MD is async and long-running (minutes to hours per simulation), so a persistent pod fits better than a scale-to-zero serverless endpoint — the engine submits a job and polls get_job_status while the pod keeps working.

Prerequisites

  • A Runpod account (console.runpod.io).
  • The engine running somewhere. You will point it at the pod's proxy URL.

Create a template and deploy a pod

  1. Templates → New Template:
  2. Container Image: ghcr.io/novomcp/gromacs-md:latest (GHCR is public — no registry credentials needed).
  3. Expose HTTP Ports: 8021. Runpod's reverse proxy publishes it at a public HTTPS URL; only one HTTP port per pod, which is all this service needs.
  4. Deploy → choose a GPU pod (L40S or A100 recommended; L4/A10G is the practical minimum, ~8 GB GPU memory per simulation) → select your template → Deploy.
  5. Once the pod is running, its endpoint is https://<POD_ID>-8021.proxy.runpod.net.

Wire into the engine

export GROMACS_MD_URL=https://<POD_ID>-8021.proxy.runpod.net

Verify

curl -s "$GROMACS_MD_URL/health"
# {"status":"healthy","gpu_available":true,"gromacs_version":"2023.5"}

End to end, run_molecular_dynamics returns a job id immediately and you poll get_job_status — the pod runs the simulation in the background, which is exactly why a persistent pod (not serverless) is the right shape here.

Cost and access

  • A persistent pod bills for GPU time while it runs (per-hour). Stop the pod when you are not running MD; for occasional use, a spot / community-cloud GPU is the cheapest.
  • The serverless option suits short request/response services; a multi-minute MD job is not that, so use a pod.
  • Security: the proxy URL is public (an obscure pod id, but not authenticated), and the MD service has no auth of its own. Treat the URL as a secret and set a Runpod spend cap so a leaked URL can't run up an unbounded bill.

Notes

Verified against Runpod's current docs (September 2026): custom pod templates. This guide is reference-quality and has not yet been live-tested end to end.