How C3 works

C3 runs your code on a GPU in a data centre. You point it at a project folder, say what hardware you want, and it provisions the machine, sets up the environment, runs your script, saves the output and shuts the machine down. You pay for the seconds it ran.

What you deploy

One of three things, chosen in .c3:

• A Python project. C3 installs your pyproject.toml dependencies with uv and runs your script in that environment. The environment is cached, so the second job starts faster than the first.
• A container. C3 pulls a public Docker Hub or GitHub Container Registry image and runs your script inside it. Use this for system packages, other languages or a specific CUDA stack.
• A plain Bash script. C3 runs it on the machine as is. Whatever it needs, it installs itself.

Whichever you choose, the whole project folder goes with it, and the script runs from that folder on the machine. See Environments.

Deploy, then walk away

c3 deploy uploads the folder and returns a job ID within seconds. From then on the job is C3's problem, not your laptop's. Close the lid, lose the wifi, go home. The job queues for a machine, runs to completion, and its output is saved in C3's storage.

Come back whenever you like:

c3 squeue              # what is queued, running and finished
c3 logs job_abc123     # what it printed
c3 pull job_abc123     # download its results

The same jobs show in the dashboard and to any coding agent connected through the MCP server. If you would rather watch, c3 deploy -f streams the logs live, and closing the terminal does not stop the job.

If you have used Slurm: c3 deploy is sbatch, c3 squeue is squeue, c3 cancel is scancel, and .c3 replaces the #SBATCH header. The difference is that there is no shared filesystem and no login node. Everything the job needs goes in with it, and everything it produces comes out as artifacts.

Files in, files out

	Workspace	Dataset	Artifacts
What	Your code: .c3, the script, source, pyproject.toml	Inputs: measurements, images, model weights	Outputs: results, plots, checkpoints
How it gets there	Uploaded automatically by every c3 deploy	Uploaded once with c3 data cp, mounted with datasets:	Written by the job to $C3_ARTIFACTS_DIR or an output: directory
On the machine	The working directory	The mount path, such as /data/my-data	Collected after the script exits

Three rules cover most mistakes:

• The workspace is everything under .c3. It is hashed on every deploy and staged onto every machine. That is fine for code and wrong for data. Anything over a few hundred megabytes, shared between projects, or not written by you belongs in a dataset: upload it once, mount it from any job.
• Only artifacts survive. Files written anywhere else on the machine are gone when the job ends. Write results to $C3_ARTIFACTS_DIR or a directory listed under output:. See Artifacts.
• Results can feed the next job. Mount /jobs/<job-id> as a dataset and the next job reads the previous one's output without it ever coming back to your laptop.

Hardware

Ask for a class and C3 picks the cheapest available machine in it: l40 (48 GB, the default), a100 (80 GB) or h100 (80 GB). Or name an exact profile from c3 list --all, including CPU-only machines with up to 96 cores and 384 GB of RAM. C3 buys from several providers and routes each job to whichever has the machine. See Hardware and capacity.

Job lifecycle

State	Meaning
PENDING	Accepted, waiting for a machine
SCHEDULING	Machine assigned, environment being prepared
RUNNING	Your script is executing. Billing starts here
SUCCEEDED	Script exited 0 and results were collected
FAILED	Script exited non-zero, or C3 could not run it
CANCELED	Stopped by c3 cancel
TIMED_OUT	Hit the time limit

c3 squeue shows SUCCEEDED as COMPLETED, and as SYNCED once you have run c3 pull.

What it costs

Per second while RUNNING, at the hourly rate of the machine the job landed on. Queueing, provisioning and result upload are free. Credit is reserved when you submit and refunded if the job never ran. New accounts start with £10. Your plan sets how many machines you can run at once. See Pricing and plans.

Glossary

Term	Meaning
Class	A family of GPUs you can request: l40, a100, h100
Profile	An exact machine type, such as h100-80gb or cpu-d3-96vcpu-384gb
Provider	The data centre operator that supplied a machine
Workspace	The files under .c3 that c3 deploy uploads
Dataset	Data uploaded once with c3 data cp, versioned, mountable in any job
Artifacts	Files a job wrote to $C3_ARTIFACTS_DIR or an output: directory
Chip	One running machine, GPU or CPU. Plans limit chips in use at once
Walltime	The time limit in .c3. Default one hour