runpodctl

Runpodctl

Manage GPU pods, serverless endpoints, templates, volumes, and models.

Install

curl -sSL https://cli.runpod.net | bash (any platform) or brew install runpod/runpodctl/runpodctl. Manual binaries, Windows/Linux steps, and the version caveat (--model-reference + multi-volume need v2.4.0+): reference/install.md.

Old runpodctl builds silently lack newer flags/behaviors (e.g. --model-reference doesn't exist before v2.4.0) and produce confusing downstream errors — and the Homebrew tap can lag well behind. So, before any work:

• Update to the latest build — check runpodctl version, then run runpodctl update (or reinstall from the latest release).
• Pin to one recent version for the whole task.
• Never switch between an old and a new binary mid-task (that flip-flop is a known failure).
• Verify once — runpodctl version shows the current build before you continue.

Quick start

runpodctl update                    # FIRST: get on the latest build — old versions cause confusing errors
runpodctl version                   # confirm the current version before doing any work
export RUNPOD_API_KEY=your_key      # Non-interactive auth (agents) — runpodctl reads this
runpodctl doctor                    # Interactive first-time setup (API key + SSH) — for humans
runpodctl --help                    # See current top-level commands
runpodctl pod create --help         # Inspect exact current flags before creating
runpodctl gpu list                  # See available GPU types
runpodctl datacenter list           # GPU availability per data center (use to co-locate GPU + volume)
runpodctl hub search vllm           # Find a hub repo
runpodctl serverless create --hub-id <id> --name "my-vllm"  # Deploy from hub
runpodctl template search pytorch   # Find a template
runpodctl pod create --template-id runpod-torch-v21 --gpu-id "NVIDIA GeForce RTX 4090"  # Create from template
runpodctl pod list                  # List your pods

Auth: an agent should export RUNPOD_API_KEY=... (non-interactive). runpodctl doctor is interactive (prompts) and also sets up SSH keys — good for a human's first run, not for scripted use.

API key: https://console.runpod.io/user/settings

Live Help Is Authoritative

Live runpodctl --help output is authoritative for exact flags, aliases, and command syntax. Use this skill for workflows, decision rules, safety notes, and common examples.

runpodctl --help
runpodctl <resource> --help
runpodctl <resource> <action> --help

Before using unfamiliar commands, inspect live help first. Do not rely on this skill as an exhaustive flag reference.

Decision Rules

• Use Hub when the user wants a known deployable app or worker such as vLLM, ComfyUI, Whisper, or a Runpod-maintained repo.
  • Picking a worker: prefer a first-party or well-adopted, recently-released worker on a broad, high-availability GPU pool. Observable signals via runpodctl hub list: --owner runpod-workers (first-party), --order-by releasedAt/updatedAt (recency), --order-by deploys/stars (adoption). Don't pin a scarce large-GPU tier a small model doesn't need.
• "Active worker" = minimum workers, not maximum. If a user asks for an "active worker," they mean --workers-min 1 (keep one worker always warm → no cold start), not --workers-max 1 (that only caps the ceiling). A warm min-1 worker is ideal for development/iteration.
• ⚠️ A min-1 worker bills continuously, even while idle (it defeats scale-to-zero). When you set --workers-min 1 for dev, you must set it back to --workers-min 0 (or delete the endpoint) when done — otherwise it quietly runs up cost.
• serverless update has no --gpu-id flag. To change an existing endpoint's GPU pool, call PATCH https://rest.runpod.io/v1/endpoints/<id> with {"gpuTypeIds":[...]} directly.
• CPU serverless endpoints: always create them with runpodctl serverless create --compute-type CPU (or the MCP server). Never use the public control REST POST https://rest.runpod.io/v1/endpoints with "computeType":"CPU" — it silently provisions a GPU endpoint instead (verified evidence in the Serverless command section below).
• Use templates when the user already has a template ID, wants reusable image/config defaults, or needs lower-level control than Hub.
• Use direct pod creation with --image when the user has a specific Docker image and does not need a saved template.
• Use serverless for request/response inference APIs and scalable workers; use pods for interactive work, notebooks, training, debugging, or long-lived sessions.
• Use CPU pods for preprocessing, file movement, lightweight scripts, and non-CUDA work. Use GPU pods when CUDA, model inference, training, or GPU memory is required.
• Do not pass GPU flags when creating CPU pods. Check runpodctl pod create --help for the current valid flag set.
• Standing up a service on a pod (Ollama, ComfyUI, a dev server)? Declare its --ports and --env at creation (they can't be added to a running pod without a reset), then follow the pod development loop in the runpod-usage skill (reference/pod-workflows.md) — SSH-exec the install, bind to 0.0.0.0, and poll the proxy URL until it answers.
• For SSH, use runpodctl pod get <pod-id> or runpodctl ssh info <pod-id> to retrieve connection details. runpodctl has no interactive-shell command — ssh info returns the connection command + key but does not connect. Run commands over SSH yourself with ssh user@host "command".
• Network volumes are location-sensitive. Check datacenter availability before attaching volumes, and use send / receive or S3-compatible storage for migrations.
• Clean up paid resources after tests: delete serverless endpoints, pods, and temporary volumes created for validation.
  • Cost guard on creation: use --terminate-after (deletes the pod); --stop-after only stops it, so disk/volume keep billing.
  • Attached volume: to delete a network volume, remove the pod using it first.

Serverless facts (context, not rules)

• Scale-to-zero billing: serverless endpoints scale to zero with --workers-min 0 (the default) — no GPU billing while idle, only per request-second; this is the right cost posture for a request/response API.
• Broken-image tell: if deployed workers go ready but jobs sit IN_QUEUE with inProgress: 0, the image is broken/mis-dispatching — the fix is to switch to a different worker rather than wait it out.
• Diagnosing it: there's no first-class serverless worker-log command, so diagnosis relies on /health worker counts.

Commands

Essentials below. Full flag menu → reference/command-reference.md (pods lifecycle, hub/template filters, registry auth, billing, SSH key management); live runpodctl <resource> <action> --help is authoritative for exact flags.

Pods

runpodctl pod list                                   # running pods (+ --all / --status / --since / --created-after)
runpodctl pod get <pod-id>                           # details incl. SSH info
runpodctl pod create --template-id <id> --gpu-id "NVIDIA GeForce RTX 4090"   # from template
runpodctl pod create --image <img> --gpu-id "NVIDIA GeForce RTX 4090"        # from image
runpodctl pod create --compute-type cpu --image ubuntu:22.04                 # CPU pod (lowercase `cpu`; serverless uses `CPU`)
runpodctl pod {start|stop|restart|reset|update|delete} <pod-id>              # lifecycle (delete aliases: rm/remove)

Hub

Browse/search the Runpod Hub (curated deployable repos).

runpodctl hub search vllm                            # find a repo (+ hub list [--type/--category/--order-by/--owner])
runpodctl hub get <listing-id|owner/name>            # repo details

Serverless (alias: sls)

runpodctl serverless list | get <endpoint-id> | delete <endpoint-id>
runpodctl serverless create --name "x" --template-id <id>       # from template
runpodctl serverless create --name "x" --hub-id <listing-id>    # from hub (+ --env KEY=VAL to override defaults)
runpodctl serverless create --hub-id <id> --gpu-id "NVIDIA GeForce RTX 4090" \
  --model-reference https://huggingface.co/<org>/<model>:main   # attach & host-cache a HF model (GPU only)
runpodctl serverless update <endpoint-id> --workers-max 5

Create from hub: --hub-id resolves the hub listing, extracts the build image and config (GPU IDs, container disk, env vars), creates an inline template, and deploys. Accepts both SERVERLESS and POD listing types. GPU IDs and env var defaults from the hub config are included automatically; override with --gpu-id and --env.

CPU serverless endpoints (the always/never rule is in Decision Rules above): create with runpodctl serverless create --compute-type CPU (optionally --instance-id, e.g. cpu3g-4-16) or the MCP server. Verified evidence for why the public REST must not be used: 2026-07-14, POST https://rest.runpod.io/v1/endpoints with "computeType":"CPU" silently returned a GPU endpoint (gpuCount:1, cpuFlavorIds:null), while runpodctl --compute-type CPU correctly returned computeType:"CPU" with instanceIds:["cpu3g-4-16"]. The MCP server drives Runpod's internal REST v2 and handles this correctly; the public control REST is v1-only (rest.runpod.io/v2 just redirects to docs). The separate runtime/invoke API https://api.runpod.ai/v2/<endpoint-id>/… (health/run/runsync/openai) is a different v2 and works fine — the v1-vs-v2 caveat here is only about the control/management REST.

Model cache (--model-reference): Attach a Hugging Face model to the endpoint by full URL with a ref, e.g. https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct:main. Runpod caches it host-side in the standard HF cache dir (/runpod-volume/huggingface-cache/hub/), so the worker loads it directly — no bake, no volume. Repeatable; works with --template-id/--hub-id, GPU only, runpodctl v2.4.0+. Full mechanics + how it compares to baking / network volume / the Model Repository: reference/model-caching.md. Worked end-to-end: golden path 20 — model-caching endpoint.

Multi-region / high-availability (--network-volume-ids): attach multiple network volumes (one per data center) so workers spread across DCs instead of being pinned to one — runpodctl serverless create --template-id <t> --network-volume-ids <v1>,<v2> --data-center-ids <dc1>,<dc2> …. Requires runpodctl ≥ v2.4.0 (older versions don't support multi-volume attach). Check runpodctl version; the Homebrew tap can lag, so prefer the GitHub releases binary. Data does not sync between volumes automatically — see golden path 10 — multi-region HA serverless.

For exact serverless flags, run runpodctl serverless <action> --help.

Templates (alias: tpl)

runpodctl template search <q>                        # find (+ template list [--type official/community/user, --all, --limit])
runpodctl template get <template-id>                 # details (README, env, ports)
runpodctl template create --name "x" --image "img" [--serverless]
runpodctl template delete <template-id>

Network Volumes (alias: nv)

runpodctl network-volume list                         # List all volumes
runpodctl network-volume get <volume-id>              # Get volume details
runpodctl network-volume create --name "x" --size 100 --data-center-id "US-GA-1"  # Create volume
runpodctl network-volume update <volume-id> --name "new"  # Update volume
runpodctl network-volume delete <volume-id>           # Delete volume

For exact network volume flags, run runpodctl network-volume <action> --help.

No storage-tier flag. create provisions the data center's default tier — there's no --type. To get a High-Performance volume, use the console (a ⚡ data center's toggle) or a raw v2 REST call (POST https://v2-rest.runpod.io/v2/network-volumes with "type":"HIGH_PERFORMANCE"). The MCP create-network-volume tool can't set it either. Tier is immutable after creation. Launch details: golden path 21.

Models (Model Repository)

runpodctl model manages the Runpod Model Repository — managed, versioned storage for your own model artifacts (upload once, distributed to workers; not pinned to a data center like a network volume). What it is, why/how, migrating off a baked-in model, and Model-Repo-vs-volume: reference/model-caching.md.

runpodctl model list                                  # List your models
runpodctl model list --all                            # List all models (not just yours)
runpodctl model list --name "llama"                   # Filter by name
runpodctl model list --provider "meta"                # Filter by provider
runpodctl model add --name "my-model" --model-path ./model   # Upload a local model dir (multipart)
runpodctl model remove --name "my-model" --owner <owner>     # Remove a model

model add supports upload sessions, versioning, metadata, and private-source credentials — see live runpodctl model add --help.

Info & SSH

runpodctl user                                       # account info + balance (alias: me)
runpodctl gpu list                                   # available GPUs (+ --include-unavailable)
runpodctl datacenter list                            # datacenters (alias: dc)
runpodctl ssh info <pod-id>                          # SSH connection details (command + key; NOT an interactive session)

ssh info gives connection details, not a session — if interactive SSH isn't available, run ssh user@host "command". Registry auth, billing history, and SSH key management (ssh add-key/remove-key) are in reference/command-reference.md.

File Transfer

runpodctl send <path>                                # prints a one-time code, then blocks until the receiver connects
runpodctl receive <code>                             # positional code (no --code flag)

Encrypted/incremental/compressed — don't pre-tar. Key gotchas: capture the first line of send stdout (the code) as it streams (background + tee), each send mints a fresh code, both sides must exit 0. Full agent flow (pod push via ssh + receive): reference/command-reference.md.

Utilities

runpodctl doctor                                      # Diagnose and fix CLI issues
runpodctl update                                      # Update CLI
runpodctl version                                     # Show version
runpodctl completion                                  # Auto-detect shell and install completion

URLs

Pod URLs

Access exposed ports on your pod:

https://<pod-id>-<port>.proxy.runpod.net

Example: https://abc123xyz-8888.proxy.runpod.net

Serverless URLs

https://api.runpod.ai/v2/<endpoint-id>/run        # Async request
https://api.runpod.ai/v2/<endpoint-id>/runsync    # Sync request
https://api.runpod.ai/v2/<endpoint-id>/health     # Health check
https://api.runpod.ai/v2/<endpoint-id>/status/<job-id>  # Job status

Source & docs

• CLI source: https://github.com/runpod/runpodctl
• Releases (binaries): https://github.com/runpod/runpodctl/releases
• Docs: https://docs.runpod.io/runpodctl/overview