Examples

Things people build here

One page per use case: what it is, how it works on levers9, the code, the commands, and where to click in the dashboard.

Deploy an OpenAI-compatible LLM endpoint

Serve Qwen, Gemma or Llama with vLLM on an L40S, behind a drop-in replacement for the OpenAI API.

Train four models in parallel on L40S

Prep on CPU, fan out training to one GPU container per config, evaluate, package: a pipeline in plain Python.

Transcribe calls in batches with Whisper

Drop recordings on a volume, get diarised transcripts as a dataset, with a person correcting the drafts.

Teach a call agent with human feedback

Play the seven missions: calls in, hot spots, duels for 10 XP a pick, level up, boss fight, crown the champion, connect it to real customers.

Train on 1, 2, 4 or 8 cards

One training function; gpu_count is the only knob. Spread one run over every card of a node, or run many single-card trainings side by side with .map().

Process ten thousand inputs with .map()

One container per item, sub-second warm starts, scale back to zero when the queue drains.

Open a JupyterLab notebook on a GPU

Your volumes and secrets mounted, live CPU, memory and GPU meters, the kernel on the cluster.

Sandboxed code execution

Run LLM-generated code in gVisor-isolated containers with CPU, memory and network limits per call.