> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tandemn.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Run your first job

> Submit a first batch inference job with Tandemn.

After the server is running and the CLI is connected, submit a job with a model, an input file, and an SLO.

## Prepare a prompt file

Use OpenAI-style batch JSONL where each line is one request payload. Keep the first test small so you can confirm the end-to-end flow quickly.

```json prompts.jsonl theme={null}
{"custom_id":"req-1","method":"POST","url":"/v1/chat/completions","body":{"model":"placeholder","messages":[{"role":"user","content":"Summarize what batch inference is in one sentence."}],"max_tokens":256}}
{"custom_id":"req-2","method":"POST","url":"/v1/chat/completions","body":{"model":"placeholder","messages":[{"role":"user","content":"Give me three reasons to use heterogeneous GPUs."}],"max_tokens":256}}
```

## Preview the placement

```bash theme={null}
tandemn plan Qwen/Qwen2.5-7B-Instruct prompts.jsonl --slo 4
```

## Submit the job

```bash theme={null}
tandemn deploy Qwen/Qwen2.5-7B-Instruct prompts.jsonl --slo 4
```

The command sends the workload to the Tandemn server. Tandemn then chooses an execution plan across the available accelerated resources.

## What the arguments mean

* `Qwen/Qwen2.5-7B-Instruct` is the model identifier.
* `prompts.jsonl` is the batch input file.
* `--slo 4` is a four-hour deadline.

## Monitor progress

```bash theme={null}
tandemn progress
tandemn web
```

> Use a model that your Tandemn deployment is configured to run. If a model cannot be scheduled, ask your administrator which models are currently available.

## If something fails

Start with the basics:

* Run `tandemn check`.
* Confirm `TD_SERVER_URL` points at the right server.
* Confirm the JSONL file exists and is readable.
* Confirm the model is available in your environment.

See [Troubleshooting](/admin/troubleshooting) for more setup checks.