Not every GPU job is a big training run. Batch processing, evals, and quick experiments need compute for a short window. Lanes Compute is built for that on-and-off pattern.
Lanes Compute is a GPU service in limited early access, and its billing model is the feature here: environments spin up in seconds, bill per second, and stop billing at teardown. A quick experiment costs cents, so trying something, seeing the result, and trying again runs at the pace of your ideas rather than your infrastructure.
How Lanes Compute does it
- Spin up per task. Grab a GPU for the job, release it when done. Environments start in seconds, so the loop is tight enough to run per experiment rather than per day, and nothing has to be kept warm between jobs.
- Per-second billing. A ten-minute experiment costs like ten minutes. The meter runs while the environment is live, stops at teardown, and nothing accrues after.
- Any size. One card for a quick test, many for a large batch. L40S covers lighter jobs, A100 through H200 SXM cover heavier ones, and multi-node clusters exist for the batches that outgrow one box.
- Your stack. Bring the container and frameworks the job needs. PyTorch, JAX, and anything else that runs in a container work out of the box, so a batch job is the same artifact you run everywhere else.
Two details keep the loop fast in practice. Datasets sit on persistent network volumes, so each job mounts its data instead of re-fetching it, and results and checkpoints survive teardown. And because the interface is a CLI and API that drop into existing tooling, a sweep can run unattended from a script or an agent: stand up, run, collect, tear down, repeat.
Why it matters
Experiments stay cheap and fast when you only pay for the minutes you use, so you can run more of them.
How many experiments a team runs is set by friction more than curiosity. When a test needs a request to whoever owns the cluster, fewer tests happen; when it needs cents and a container, more do. Per-task GPUs also unclog the calendar: an eval pass before a release is a job you start, not a slot you book.
Set it up
- Request access. Lanes Compute is in limited early access; request it via the form on the Compute page.
- Describe the jobs. The kinds of batches and experiments you run and the frameworks they use. That is enough to match hardware.
- Get onboarded. Usually within a day or two.
- Adopt the loop. Spin up for the job, run it from your container, write results to a volume, tear down. Repeat as often as ideas arrive.
Further reading
- Right-size every job and pay per second: picking the card once the loop is in place.
- GPUs for agent workloads: the same loop, driven by agents instead of people.
- Lanes Compute vs Vast.ai: how it compares with a GPU marketplace.