List GPU nodes available to rent right now, with price per hour, GPU model, VRAM, CUDA compute capability, and country. Rent by the returned node_id. Read-only, no cost. For modern LLM work prefer compute_cap >= 8.9 (FP8).

Get the account's credit balance (rentals are billed per minute against it). Read-only, no cost.

Start renting a GPU. SPENDS CREDITS (per-minute billing) — confirm with the user before calling. Asynchronous: returns a rental_id with status 'pending'. Provisioning + the secure tunnel take ~20-40 seconds. After creating, call get_rental ONCE to fetch the connection URL — it waits internally for startup and returns when ready, so do NOT poll it in a tight loop. DURATION: the rental is open-ended by default (runs until the user stops it with destroy_rental) — use open-ended as the default. When starting one, briefly tell the user it's open-ended and bills per minute until stopped, and that they can set an 'hours' auto-stop cap instead if they prefer; only pass 'hours' when the user asks for a time limit. An idempotency key is generated automatically so accidental repeats don't start a second GPU.

Get one rental's status and connection details (Jupyter URL + token). After create_rental, call this to get the URL. It WAITS internally (up to ~24s) for the rental to finish coming up and returns as soon as it's ready — so you usually only need ONE call, at most two. Do NOT call it repeatedly in a tight loop. Provisioning plus the secure tunnel take ~20-40 seconds total; the URL is ready when connection_pending is false. Read-only.

List the account's rentals. By default only active ones (what's running and billing). Pass status='all' for full history. Read-only.

Stop a rental — stops the GPU and STOPS BILLING. This is the money brake; safe to call twice. Confirm with the user unless they clearly asked to stop it.