GPU workers that spin up when a request arrives and disappear when it's done. No idle instances, no capacity planning, no invoice for a machine that sat there overnight.
Serverless sits on the same global GPU marketplace as everything else we run — from consumer cards to B200 nodes. You describe the workload; we pick the hardware, scale it, and bill by the second.
Define the image and the scaling rules once. Worker lifecycle, placement and replacement are handled for you.
The same marketplace price you'd pay renting the card directly. No serverless surcharge, no pro tier, no per-request fee.
RTX 4090 for cheap throughput, H200 or B200 when the model demands it. Pin a GPU type or let us match one to the job.
Place workers near your users to cut latency, or inside a region because the data isn't allowed to leave it.
Inference traffic doesn't arrive evenly. Workers follow the curve instead of sitting at peak capacity all day.
Scale on queue depth, request rate or latency target — whichever actually predicts your load. Down to zero when the queue empties.
Keep a few workers pre-loaded so the first request after a quiet period doesn't wait on a container pull. Set the floor to zero if cost matters more than the first-token time.
vLLM, TGI, ComfyUI, or your own image from a private registry. If it runs in Docker and listens on a port, it runs here.
Per-worker logs and utilisation, plus SSH into a live worker when something only reproduces in production.
Image, scaling policy and worker types live in your repo, not in a dashboard someone clicked through once and forgot.
Route cheap requests to 4090s and heavy ones to H200s behind a single endpoint, with its own scaling rule per type.
Serverless GPU on Linkhome AI starts workers when requests arrive and scales to zero when they stop, so you pay only for the seconds of AI compute you use.
It runs on the same global GPU rental marketplace as the rest of our AI infrastructure — any container, any GPU, billed per second.
Linkhome AI